{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
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       "      <td>9502</td>\n",
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       "      <td>POLYGON ((-101.73553 34.21051, -101.73553 34.2...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
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       "      <td>48219950400</td>\n",
       "      <td>9504</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>12691656</td>\n",
       "      <td>5302</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>POLYGON ((-102.41616 33.56994, -102.41616 33.5...</td>\n",
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       "      <td>48</td>\n",
       "      <td>219</td>\n",
       "      <td>950300</td>\n",
       "      <td>48219950300</td>\n",
       "      <td>9503</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>12186639</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-102.36763 33.58398, -102.36763 33.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>219</td>\n",
       "      <td>950100</td>\n",
       "      <td>48219950100</td>\n",
       "      <td>9501</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>214157569</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-102.28192 33.72410, -102.28190 33.7...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48</td>\n",
       "      <td>219</td>\n",
       "      <td>950600</td>\n",
       "      <td>48219950600</td>\n",
       "      <td>9506</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>358638163</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-102.31826 33.41007, -102.31598 33.4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20 NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        48        189    950200  48189950200   9502  Census Tract   G5020   \n",
       "1        48        219    950400  48219950400   9504  Census Tract   G5020   \n",
       "2        48        219    950300  48219950300   9503  Census Tract   G5020   \n",
       "3        48        219    950100  48219950100   9501  Census Tract   G5020   \n",
       "4        48        219    950600  48219950600   9506  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20    ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S    6306913         0  ...       84       84        0        0   \n",
       "1          S   12691656      5302  ...       57        0        0       57   \n",
       "2          S   12186639         0  ...       51        0        0       51   \n",
       "3          S  214157569         0  ...        0        0        0        0   \n",
       "4          S  358638163         0  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        6        0        0        6   \n",
       "1        0        0        0        0        0   \n",
       "2        0        0        0        0        0   \n",
       "3        0        0        0        0        0   \n",
       "4        0        0        0        0        0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-101.73553 34.21051, -101.73553 34.2...  \n",
       "1  POLYGON ((-102.41616 33.56994, -102.41616 33.5...  \n",
       "2  POLYGON ((-102.36763 33.58398, -102.36763 33.5...  \n",
       "3  POLYGON ((-102.28192 33.72410, -102.28190 33.7...  \n",
       "4  POLYGON ((-102.31826 33.41007, -102.31598 33.4...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8/4/22 - augmentation from enacted_analysis to pull Texas CVAP data for racial dislocation\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "tractPopFile = gpd.read_file(\"state_map_files/tx_pl2020_t.dbf\")\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2fc0e15f-770c-4afb-90ab-20a5fd5b6a70",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0. :  #excluding linestrings\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "def r3(x):\n",
    "    y = round(x,3)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "307c7903-9e7a-4765-b03b-479550260a79",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>C_TOT20</th>\n",
       "      <th>CTOTMOE</th>\n",
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       "      <th>CVAPHSPMOE</th>\n",
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       "      <td>048189950200</td>\n",
       "      <td>Census Tract 9502, Hale County, Texas</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Hale County</td>\n",
       "      <td>3530</td>\n",
       "      <td>593</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>048219950400</td>\n",
       "      <td>Census Tract 9504, Hockley County, Texas</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Hockley County</td>\n",
       "      <td>4020</td>\n",
       "      <td>483</td>\n",
       "      <td>1280</td>\n",
       "      <td>325</td>\n",
       "      <td>4</td>\n",
       "      <td>23.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>1685</td>\n",
       "      <td>182</td>\n",
       "      <td>48</td>\n",
       "      <td>219</td>\n",
       "      <td>9504</td>\n",
       "      <td>POLYGON ((-102.41616 33.56994, -102.41616 33.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>048219950300</td>\n",
       "      <td>Census Tract 9503, Hockley County, Texas</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Hockley County</td>\n",
       "      <td>3875</td>\n",
       "      <td>400</td>\n",
       "      <td>2050</td>\n",
       "      <td>327</td>\n",
       "      <td>30</td>\n",
       "      <td>36.78</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>36</td>\n",
       "      <td>1295</td>\n",
       "      <td>266</td>\n",
       "      <td>48</td>\n",
       "      <td>219</td>\n",
       "      <td>9503</td>\n",
       "      <td>POLYGON ((-102.36763 33.58398, -102.36763 33.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>048219950100</td>\n",
       "      <td>Census Tract 9501, Hockley County, Texas</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Hockley County</td>\n",
       "      <td>1410</td>\n",
       "      <td>211</td>\n",
       "      <td>760</td>\n",
       "      <td>111</td>\n",
       "      <td>34</td>\n",
       "      <td>27.28</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
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       "      <td>219</td>\n",
       "      <td>9501</td>\n",
       "      <td>POLYGON ((-102.28192 33.72410, -102.28190 33.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>048219950600</td>\n",
       "      <td>Census Tract 9506, Hockley County, Texas</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Hockley County</td>\n",
       "      <td>1575</td>\n",
       "      <td>240</td>\n",
       "      <td>1080</td>\n",
       "      <td>163</td>\n",
       "      <td>19</td>\n",
       "      <td>23.62</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>320</td>\n",
       "      <td>104</td>\n",
       "      <td>48</td>\n",
       "      <td>219</td>\n",
       "      <td>9506</td>\n",
       "      <td>POLYGON ((-102.31826 33.41007, -102.31598 33.4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        GEOID20                                      NAME  STATE  \\\n",
       "0  048189950200     Census Tract 9502, Hale County, Texas  Texas   \n",
       "1  048219950400  Census Tract 9504, Hockley County, Texas  Texas   \n",
       "2  048219950300  Census Tract 9503, Hockley County, Texas  Texas   \n",
       "3  048219950100  Census Tract 9501, Hockley County, Texas  Texas   \n",
       "4  048219950600  Census Tract 9506, Hockley County, Texas  Texas   \n",
       "\n",
       "           COUNTY  C_TOT20  CTOTMOE  C_NHS20  CNHSMOE  C_AIA20  CAIAMOE  ...  \\\n",
       "0     Hale County     3530      593      635      253       10    28.14  ...   \n",
       "1  Hockley County     4020      483     1280      325        4    23.15  ...   \n",
       "2  Hockley County     3875      400     2050      327       30    36.78  ...   \n",
       "3  Hockley County     1410      211      760      111       34    27.28  ...   \n",
       "4  Hockley County     1575      240     1080      163       19    23.62  ...   \n",
       "\n",
       "   CVAP_AIB20  CVAPAIBMOE  CVAP_2OM20  CVAP2OMMOE  CVAP_HSP20  CVAPHSPMOE  \\\n",
       "0          10          20           0          14        1630         305   \n",
       "1           0          14           0          14        1685         182   \n",
       "2           0          14          20          36        1295         266   \n",
       "3           0          14           0          14         410         123   \n",
       "4           0          14           4           2         320         104   \n",
       "\n",
       "   STATEFP  COUNTYFP  TRACTCE  \\\n",
       "0       48       189     9502   \n",
       "1       48       219     9504   \n",
       "2       48       219     9503   \n",
       "3       48       219     9501   \n",
       "4       48       219     9506   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-101.73553 34.21051, -101.73553 34.2...  \n",
       "1  POLYGON ((-102.41616 33.56994, -102.41616 33.5...  \n",
       "2  POLYGON ((-102.36763 33.58398, -102.36763 33.5...  \n",
       "3  POLYGON ((-102.28192 33.72410, -102.28190 33.7...  \n",
       "4  POLYGON ((-102.31826 33.41007, -102.31598 33.4...  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tractVAPFile = gpd.read_file(\"state_map_files/tx_cvap_2020_t.dbf\")\n",
    "tractVAPFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "208cfbe0-8d55-4740-8c0a-6b658e94213b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 6896 rows in the CVAP file\n"
     ]
    }
   ],
   "source": [
    "tractCHisp = tractVAPFile['CVAP_HSP20']\n",
    "tractCVAP = tractVAPFile['CVAP_TOT20']\n",
    "tractCBlack = tractVAPFile['CVAP_BLK20']\n",
    "tractCGEO_ID = tractVAPFile['GEOID20']\n",
    "tractCGeom = tractVAPFile['geometry']\n",
    "print(\"there are\",len(tractCVAP),\"rows in the CVAP file\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 6896 popn tracts for TX\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"TX\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #to compare to tractCVAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #to compare to tractCHisp\n",
    "tractBlack = tractPopFile['P0030004']  \n",
    "tractGEO_ID = tractPopFile['GEOID20']  #***CHECK THAT THIS IS RIGHT\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "tractCArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "    tractCArea[t] = tractCGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "680b7697-d02a-4c7e-8ec0-f1841e6c254b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is the tract-level correlation of 2015-19 CVAP to 2020 VAP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here is the tract-level correlation of tract area, CVAP 2015-19 to 2020 Census\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is the tract-level correlation of 2015-19 CVAP to 2020 VAP\")\n",
    "plt.scatter(tractVAP,tractCVAP)\n",
    "x = [0,15000]\n",
    "y = [0,15000]\n",
    "plt.plot(x,y)\n",
    "plt.show()\n",
    "print(\"and here is the tract-level correlation of tract area, CVAP 2015-19 to 2020 Census\")\n",
    "plt.scatter(tractArea, tractCArea)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a744ec52-64a9-4138-8691-e87adab0ca82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lets dig into the scatter for CVAP vs. VAP.  Plot the ratio\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the total CVAP was 18578975 while the total VAP was 21866700 for TX\n"
     ]
    }
   ],
   "source": [
    "print(\"lets dig into the scatter for CVAP vs. VAP.  Plot the ratio\")\n",
    "cRatio = [1.] * nTracts\n",
    "for t in range(nTracts):\n",
    "    if tractVAP[t] > 0 :  #avoid div by zero\n",
    "        cRatio[t] = tractCVAP[t] / tractVAP[t]\n",
    "plt.scatter(tractVAP,cRatio)\n",
    "plt.show()\n",
    "print(\"the total CVAP was\",np.sum(tractCVAP),\"while the total VAP was\",np.sum(tractVAP),\"for\",STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f4fc3ebe-aba1-42a7-8858-cd3b1c999942",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is the tract-level correlation of 2015-19 CVAP to 2020 VAP for Hispanics\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is the tract-level correlation of 2015-19 CVAP to 2020 VAP for Hispanics\")\n",
    "plt.scatter(tractHisp,tractCHisp)\n",
    "x = [0,15000]\n",
    "y = [0,15000]\n",
    "plt.plot(x,y)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d1e0a9fa-3bda-473c-a1f6-812110aba5ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and now the ratio of Hispanic CVAP to Hispanic VAP from these two files\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the total Hisp CVAP was 5671690 while the total Hisp VAP was 7907319 for TX\n"
     ]
    }
   ],
   "source": [
    "print(\"and now the ratio of Hispanic CVAP to Hispanic VAP from these two files\")\n",
    "cHRatio = [1.] * nTracts\n",
    "for t in range(nTracts):\n",
    "    if tractHisp[t] > 0 :  #avoid div by zero\n",
    "        cHRatio[t] = tractCHisp[t] / tractHisp[t]\n",
    "plt.scatter(tractHisp,cHRatio)\n",
    "plt.show()\n",
    "print(\"the total Hisp CVAP was\",np.sum(tractCHisp),\"while the total Hisp VAP was\",np.sum(tractHisp),\"for\",STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fc632900-8c58-4d3a-9def-5b2652773ade",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is the tract-level correlation of 2015-19 CVAP to 2020 VAP for Blacks\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the total Black CVAP was 2512289 while the total Black VAP was 2627714 for TX\n"
     ]
    }
   ],
   "source": [
    "print(\"here is the tract-level correlation of 2015-19 CVAP to 2020 VAP for Blacks\")\n",
    "plt.scatter(tractBlack,tractCBlack)\n",
    "x = [0,5000]\n",
    "y = [0,5000]\n",
    "plt.plot(x,y)\n",
    "plt.show()\n",
    "print(\"the total Black CVAP was\",np.sum(tractCBlack),\"while the total Black VAP was\",np.sum(tractBlack),\"for\",STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CNTY</th>\n",
       "      <th>COLOR</th>\n",
       "      <th>PREC</th>\n",
       "      <th>PCTKEY</th>\n",
       "      <th>CNTYKEY</th>\n",
       "      <th>G20VR</th>\n",
       "      <th>G20SSVR</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>...</th>\n",
       "      <th>G20SSCRBUS</th>\n",
       "      <th>G20SSCDTRI</th>\n",
       "      <th>G20SSCLOXF</th>\n",
       "      <th>G20SCCRRIC</th>\n",
       "      <th>G20SCCDFRI</th>\n",
       "      <th>G20SCCRYEA</th>\n",
       "      <th>G20SCCDCLI</th>\n",
       "      <th>G20SCCRNEW</th>\n",
       "      <th>G20SCCDBIR</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>113</td>\n",
       "      <td>7</td>\n",
       "      <td>1104</td>\n",
       "      <td>1131104</td>\n",
       "      <td>57</td>\n",
       "      <td>2745</td>\n",
       "      <td>39.5</td>\n",
       "      <td>221</td>\n",
       "      <td>1173</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>195</td>\n",
       "      <td>1157</td>\n",
       "      <td>32</td>\n",
       "      <td>216</td>\n",
       "      <td>1172</td>\n",
       "      <td>214</td>\n",
       "      <td>1169</td>\n",
       "      <td>219</td>\n",
       "      <td>1162</td>\n",
       "      <td>POLYGON ((1314208.406 1178220.110, 1314211.847...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201</td>\n",
       "      <td>2</td>\n",
       "      <td>0312</td>\n",
       "      <td>2010312</td>\n",
       "      <td>101</td>\n",
       "      <td>3973</td>\n",
       "      <td>11.3</td>\n",
       "      <td>1124</td>\n",
       "      <td>1460</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>1190</td>\n",
       "      <td>1290</td>\n",
       "      <td>54</td>\n",
       "      <td>1194</td>\n",
       "      <td>1343</td>\n",
       "      <td>1152</td>\n",
       "      <td>1373</td>\n",
       "      <td>1170</td>\n",
       "      <td>1345</td>\n",
       "      <td>POLYGON ((1432565.993 851290.217, 1432575.099 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>351</td>\n",
       "      <td>4</td>\n",
       "      <td>0003</td>\n",
       "      <td>3510003</td>\n",
       "      <td>176</td>\n",
       "      <td>626</td>\n",
       "      <td>1.1</td>\n",
       "      <td>412</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>387</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>390</td>\n",
       "      <td>29</td>\n",
       "      <td>382</td>\n",
       "      <td>32</td>\n",
       "      <td>386</td>\n",
       "      <td>31</td>\n",
       "      <td>POLYGON ((1602738.373 1008175.555, 1602745.401...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>181</td>\n",
       "      <td>4</td>\n",
       "      <td>0304</td>\n",
       "      <td>1810304</td>\n",
       "      <td>91</td>\n",
       "      <td>3058</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1290</td>\n",
       "      <td>676</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>1331</td>\n",
       "      <td>597</td>\n",
       "      <td>39</td>\n",
       "      <td>1335</td>\n",
       "      <td>632</td>\n",
       "      <td>1339</td>\n",
       "      <td>617</td>\n",
       "      <td>1347</td>\n",
       "      <td>619</td>\n",
       "      <td>POLYGON ((1312523.436 1279889.507, 1312544.741...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201</td>\n",
       "      <td>2</td>\n",
       "      <td>0877</td>\n",
       "      <td>2010877</td>\n",
       "      <td>101</td>\n",
       "      <td>5743</td>\n",
       "      <td>27.1</td>\n",
       "      <td>1352</td>\n",
       "      <td>2554</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>1316</td>\n",
       "      <td>2466</td>\n",
       "      <td>105</td>\n",
       "      <td>1359</td>\n",
       "      <td>2516</td>\n",
       "      <td>1379</td>\n",
       "      <td>2495</td>\n",
       "      <td>1382</td>\n",
       "      <td>2490</td>\n",
       "      <td>POLYGON ((1409146.792 864246.161, 1409155.944 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CNTY  COLOR  PREC   PCTKEY  CNTYKEY  G20VR  G20SSVR  G20PRERTRU  \\\n",
       "0   113      7  1104  1131104       57   2745     39.5         221   \n",
       "1   201      2  0312  2010312      101   3973     11.3        1124   \n",
       "2   351      4  0003  3510003      176    626      1.1         412   \n",
       "3   181      4  0304  1810304       91   3058      4.5        1290   \n",
       "4   201      2  0877  2010877      101   5743     27.1        1352   \n",
       "\n",
       "   G20PREDBID  G20PRELJOR  ...  G20SSCRBUS  G20SSCDTRI  G20SSCLOXF  \\\n",
       "0        1173           7  ...         195        1157          32   \n",
       "1        1460          21  ...        1190        1290          54   \n",
       "2          28           0  ...         387          28           5   \n",
       "3         676          29  ...        1331         597          39   \n",
       "4        2554          43  ...        1316        2466         105   \n",
       "\n",
       "   G20SCCRRIC  G20SCCDFRI  G20SCCRYEA  G20SCCDCLI  G20SCCRNEW  G20SCCDBIR  \\\n",
       "0         216        1172         214        1169         219        1162   \n",
       "1        1194        1343        1152        1373        1170        1345   \n",
       "2         390          29         382          32         386          31   \n",
       "3        1335         632        1339         617        1347         619   \n",
       "4        1359        2516        1379        2495        1382        2490   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((1314208.406 1178220.110, 1314211.847...  \n",
       "1  POLYGON ((1432565.993 851290.217, 1432575.099 ...  \n",
       "2  POLYGON ((1602738.373 1008175.555, 1602745.401...  \n",
       "3  POLYGON ((1312523.436 1279889.507, 1312544.741...  \n",
       "4  POLYGON ((1409146.792 864246.161, 1409155.944 ...  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/tx_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CNTY</th>\n",
       "      <th>COLOR</th>\n",
       "      <th>PREC</th>\n",
       "      <th>PCTKEY</th>\n",
       "      <th>CNTYKEY</th>\n",
       "      <th>G20VR</th>\n",
       "      <th>G20SSVR</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>...</th>\n",
       "      <th>G20SSCRBUS</th>\n",
       "      <th>G20SSCDTRI</th>\n",
       "      <th>G20SSCLOXF</th>\n",
       "      <th>G20SCCRRIC</th>\n",
       "      <th>G20SCCDFRI</th>\n",
       "      <th>G20SCCRYEA</th>\n",
       "      <th>G20SCCDCLI</th>\n",
       "      <th>G20SCCRNEW</th>\n",
       "      <th>G20SCCDBIR</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>113</td>\n",
       "      <td>7</td>\n",
       "      <td>1104</td>\n",
       "      <td>1131104</td>\n",
       "      <td>57</td>\n",
       "      <td>2745</td>\n",
       "      <td>39.5</td>\n",
       "      <td>221</td>\n",
       "      <td>1173</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>195</td>\n",
       "      <td>1157</td>\n",
       "      <td>32</td>\n",
       "      <td>216</td>\n",
       "      <td>1172</td>\n",
       "      <td>214</td>\n",
       "      <td>1169</td>\n",
       "      <td>219</td>\n",
       "      <td>1162</td>\n",
       "      <td>POLYGON ((-96.64136 32.73404, -96.64136 32.733...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201</td>\n",
       "      <td>2</td>\n",
       "      <td>0312</td>\n",
       "      <td>2010312</td>\n",
       "      <td>101</td>\n",
       "      <td>3973</td>\n",
       "      <td>11.3</td>\n",
       "      <td>1124</td>\n",
       "      <td>1460</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>1190</td>\n",
       "      <td>1290</td>\n",
       "      <td>54</td>\n",
       "      <td>1194</td>\n",
       "      <td>1343</td>\n",
       "      <td>1152</td>\n",
       "      <td>1373</td>\n",
       "      <td>1170</td>\n",
       "      <td>1345</td>\n",
       "      <td>POLYGON ((-95.51892 29.74333, -95.51883 29.743...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>351</td>\n",
       "      <td>4</td>\n",
       "      <td>0003</td>\n",
       "      <td>3510003</td>\n",
       "      <td>176</td>\n",
       "      <td>626</td>\n",
       "      <td>1.1</td>\n",
       "      <td>412</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>387</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>390</td>\n",
       "      <td>29</td>\n",
       "      <td>382</td>\n",
       "      <td>32</td>\n",
       "      <td>386</td>\n",
       "      <td>31</td>\n",
       "      <td>POLYGON ((-93.66637 31.08457, -93.66630 31.084...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>181</td>\n",
       "      <td>4</td>\n",
       "      <td>0304</td>\n",
       "      <td>1810304</td>\n",
       "      <td>91</td>\n",
       "      <td>3058</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1290</td>\n",
       "      <td>676</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>1331</td>\n",
       "      <td>597</td>\n",
       "      <td>39</td>\n",
       "      <td>1335</td>\n",
       "      <td>632</td>\n",
       "      <td>1339</td>\n",
       "      <td>617</td>\n",
       "      <td>1347</td>\n",
       "      <td>619</td>\n",
       "      <td>POLYGON ((-96.62623 33.65217, -96.62603 33.651...</td>\n",
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       "    <tr>\n",
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       "      <td>2010877</td>\n",
       "      <td>101</td>\n",
       "      <td>5743</td>\n",
       "      <td>27.1</td>\n",
       "      <td>1352</td>\n",
       "      <td>2554</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>1316</td>\n",
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       "      <td>2516</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CNTY  COLOR  PREC   PCTKEY  CNTYKEY  G20VR  G20SSVR  G20PRERTRU  \\\n",
       "0   113      7  1104  1131104       57   2745     39.5         221   \n",
       "1   201      2  0312  2010312      101   3973     11.3        1124   \n",
       "2   351      4  0003  3510003      176    626      1.1         412   \n",
       "3   181      4  0304  1810304       91   3058      4.5        1290   \n",
       "4   201      2  0877  2010877      101   5743     27.1        1352   \n",
       "\n",
       "   G20PREDBID  G20PRELJOR  ...  G20SSCRBUS  G20SSCDTRI  G20SSCLOXF  \\\n",
       "0        1173           7  ...         195        1157          32   \n",
       "1        1460          21  ...        1190        1290          54   \n",
       "2          28           0  ...         387          28           5   \n",
       "3         676          29  ...        1331         597          39   \n",
       "4        2554          43  ...        1316        2466         105   \n",
       "\n",
       "   G20SCCRRIC  G20SCCDFRI  G20SCCRYEA  G20SCCDCLI  G20SCCRNEW  G20SCCDBIR  \\\n",
       "0         216        1172         214        1169         219        1162   \n",
       "1        1194        1343        1152        1373        1170        1345   \n",
       "2         390          29         382          32         386          31   \n",
       "3        1335         632        1339         617        1347         619   \n",
       "4        1359        2516        1379        2495        1382        2490   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-96.64136 32.73404, -96.64136 32.733...  \n",
       "1  POLYGON ((-95.51892 29.74333, -95.51883 29.743...  \n",
       "2  POLYGON ((-93.66637 31.08457, -93.66630 31.084...  \n",
       "3  POLYGON ((-96.62623 33.65217, -96.62603 33.651...  \n",
       "4  POLYGON ((-95.75613 29.86868, -95.75604 29.868...  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #TX's precinct file in wrong CRS\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64b29ecf-f629-42ef-9d21-66aec1af8ed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NOT YET IMPLEMENTED -- WOULD NEED TO PASS GEOMETRY DATA TO ENABLE ***\n",
    "#DRAFT 7/8/22 - define a function to compute the HD-expected avg and std devn of a demographic property\n",
    "# ... for a geographic collection of people, across a coherent set of home districts\n",
    "def get_sd(t, property, tractPop, tractGeom, tractArea,tractUse, avgDistrictPop):\n",
    "    nTrax = len(tractPop)\n",
    "    propertyAvg = 0.\n",
    "    propertyVar = 0.   #stop here for now\n",
    "    for tt in range(nTrax):\n",
    "        intersxn = tractGeom[t].intersection(HDPoly[tt])\n",
    "        weight[tt] = intersxn.area/tractArea[t] * tractPop[tt]/avgDistrictPop / tractUse[t]\n",
    "        propertyAvg += weight * HDvGOP[tt]\n",
    "    for tt in range(nTracts):\n",
    "        HDvariance[t] += weight[tt] * (HDvGOP[t] - HDvGOP[tt])**2\n",
    "        drawnVariance[t] += weight[tt] * (drawn_vGOP[t] - HDvGOP[tt])**2\n",
    "        \n",
    "        HDsd[t] = HDvariance[t] ** 0.5\n",
    "        drawn_sd[t] = drawnVariance[t] ** 0.5\n",
    "        return sd[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9014 0.5283093020704941 11149851 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "vtdArea = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d04f4b91-1a9e-43fd-8ff0-34490d64c3a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 38 enacted Districts for TX with avg district Pop 766986.974\n"
     ]
    }
   ],
   "source": [
    "#Now, pull in the enacted map data\n",
    "enactedFile = gpd.read_file(\"enacted_map_files/TX_CD_2021.dbf\") #STANDARD FILE *****\n",
    "enactedFile.head()\n",
    "#enactedFile = enactedFile.to_crs(tractPopFile.crs)  #MN's map file in wrong CRS\n",
    "enactedGeom = enactedFile['geometry']\n",
    "nDistricts = len(enactedGeom)\n",
    "avgDistrictPop = np.sum(tractPop)/nDistricts\n",
    "print(\"there are {0} enacted Districts for {1} with avg district Pop {2}\".format(nDistricts, STATE,r3(avgDistrictPop)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bbb53828-aefc-4cbd-8fbf-8b60dbc3497e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 38 enacted Districts for TX\n"
     ]
    }
   ],
   "source": [
    "enactedFile = enactedFile.to_crs(tractPopFile.crs)  #for NY, enacted geom in wrong CRS\n",
    "enactedGeom = enactedFile['geometry']\n",
    "nDistricts = len(enactedGeom)\n",
    "print(\"there are {0} enacted Districts for {1}\".format(nDistricts, STATE) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4k3SGFWdW8L++/6OxV2PWhq0lzZzGfY3uw9vRu8jXUF7oAl7NqOwulLFjx+Z8gY8c0arhHDx4kKeeeopLYadwd3dnzMQ3cXNzq2BLqy5XQ0kLbhcWFsaBAwfo3LkzMfERnD9/is6dO+Pg4MCMGTPo2LFjoedyt3cvtn3btm1jxYoV/PPPP2RmZpKcnMyYMWP4+eefb2hrVIw80uwRJrWbxKXUSxyJO0JUahQp5hQSMhM4HHuYn47+hFXmTu3b0qclfYL68M0d3+Dn5MeSU0vYFbmLgzEHAXhm/TO52s8MmcnhRw4X+1rKmsr5LdYpMyr7CPzRRx9lwoQJPPzwwznbxo0bx4wZM7i8Zzkrth3lk08+4b333iugl1uHrN3/Yr5w8sYdeUSPOdx2F8bA+oX2mTMCV/JX8NTUVIYPH84XX3yBm5sbqrSSmZnOzp072bNnDyNGjODs2bPFCkfNzMykR48eZGVlYbFYuO+++3jnnXduaDdt2jSmTZsGwKZNm5gxY0ae4g1gFEZUqSKEINA1ME83jUW1EJsRy6XUS3y0+yNOxJ9ASsmX+7/MadPOtx3NvZtzd727eX/X+wCYFBMuJhcSshIACIkOyXEPVRZ0Aa9mVHYB79GjB2FhYbm2hYaG0qNHDxbvWU7j+sEs/OMP3rv+i5+XkFTBWPdrUdNTOPvoc6AW9Tq+watHPXz/txxRhDusLbM3Yu+yHaNRYDCCwajg4GykyX13MfyBBxg9ejTDhg0DwM3Fm+b1OrNi03pq+fqhKAox0dG4O6lYARQjimJEMdghFAMGxYQQSi6Bt7e3Z8OGDbi4uGA2m+nevTt33XUXXbp0IT01msuxx/D2rI+be9FXipoUE2bVXGAbo2LEz9kPP2c/Ft+9OGd7VFoUuyJ3cTj2MKHxofx5+s9cIZKrh6+mhmMNfjr6E5/t+4yHVj1U6UbhuoBXMyq7gOdFixYtWLFiBYoi+O9wGGfOnOHdd98q/MBKhnd0HD02b8Foi02+kpfrBnm2bdDu+gUujd3wfmpCHj1ePTLuu1mknogjfstZvM4fw1Q/73hrAN+mdfDZdYS4NE/UFAMWacQqjZilE1JK3l80gqbNm/Piiy/mHNO8WTvCT4YR/ntv9iXuIy09nY/Xj2KtOSbf8wC4qpKlA34mOv4k0YlniE65RHT6ZS4lRXPm8hFeWTkGy3EHUm13A+2lHT89ui/n+F69etGrV698+zcqxkIFPD/8nP0Y0mAIQxpoi6EsqoW1YWuZvHUyAH0X983Vvp1vuxKdpyzRBbyaURWX0s+dO5eJEycSFX6etg1rYW8y0C3oyiKRok5rygJfFr2vPNoUyQRJzPkUjFYrzl0b4OBil9sNcuW57e+VPUIx4PnsFExN2hfYu9NdY4ifNoHL89YjTHYFtq3Rpg0jv2hzw/aI//5jxif7WLpuHUEHT7Jh7VoUOzs+/PBD5v78OQ898jAfLH8Ug8We1957jTirtiqyIw7c7t0KVVqRqooVFVVa+V/SEVIUQb+1D129TFVy5q0zmKOzadzHh46N/fC198DXsQYro7aTbixelUeLtKCI0qkMaVSMDKg7gJrONTmffJ5NFzexJXwL3g7eTO40OSfEsTKhC3g1oyoKeJMmTVi7di0AJ0+e5PiYMfQd+0YhR1U+9id9Cjv24jb5UzwaNy78gOJi0e6uhCnviA9LUhxpMXEYhRk1OxNrZiaKsxdujbSiE261A6nvn813T63ELB1zjuvRoj4unu6sXLGcRSvWEvOPkZ+Vt0lVNd/wcTWdOQO/Ryi5hbTRtmmciD6Ir7MfNd2CqOFRD1/vJng+XJ/klDSGDh3K2LZf06KFtsBn97xOJElL8a5ZguH6ZGA3gSIU2tdsT/ua7RnWcFip9VtW6AJezaiKAh4dHY2vry+qqvL+++/z1FNPVbRJJSJnVF1W/V8RcKOJ+GPH+OOb82RbtTsVR0MyWVZHVK4X90ggkiDPi0gpCXDIxMND5XKcE3FmLfncH58u46dDyzh1+gzWNIVuTQeR3VFbfn+P6kjUbm+UsQaaN2+OlDJncnL1snj++isEOzs76tevz48/jsXDwwMADw8PevXqxerVq3ME3CJVjHqd9WKhC3g144qAK0rl/KKMGjWKTZs2ERsbS2BgIO+88w6pqal88803AAwbNozHHnusgq0sGdIm3WX1zkvb/MbZdVtYszkArYiWRl3/WD79awE7ToTg5ebB8i9mg9XCxA/mc/ziVowGIzXdvHm6/5NERDXJ1W9Kmj2ffPIJu35IJjM7nY+XPoV7lBMOgfYYMz24eMZKYGAgmzZtwt3dPWdysl+/fkybNg2j0cjkyZOZOnUq7733Hh4eHmRkZLBu3TomT56ccx6LtGIsZvZAWc1XBugCXs2o7CPwhQsX5rl90qRJ5WxJ6aPaxEaU0Rg8K/Yyh1o8SezmAADat4onqIkbAX36A30w9GiEi4sLDz/8MM2G3wnAqx4m+vT5JUdkT3COqc/cybxp5wBwVc7w+ZYZfLpRkplgpW3dHvh51CH8rxOkX0rjg7hjdGzVMedzZTabMZvNCCHo3/+qz7hLly788MMP9O7dG6vViqqqjBgxgsGDB+e0sUqJfSn5s6sLuoBXMyq7gN/KXBkrlpVERaV7EOvTimD3UNoO7URAlz659ucVonm9yC5ZsgSXOnUZMS6G1OgE6tw5lmEZoxAGhWnvLOCnH6aTnJ5A7Qm1SLmYTOohE+dqJJFyOJZevXoRFhbGs88+S+fOnXOdZ+7cuYwaNYoxY8bkfwFSRegCXix0Aa9m6AJeceSkICnF2/5j2w5w8PsFGCxm7FIDwQmO2LfkyNpMxLrtoAiEAEURCEWQkBhJXFwGX365B0URKIogPTEbk10CP/72AZ26dmPe4uUIoaAIwc7laxEoxGYKlma6EpEUjmfXUWSm1SJq0SKElCTtiUJNz6ZzDQd+fe4ZHpr5O6v+/p2k5Axq1/KlY6vGGAWMHj06z2tIT4/n+IXNHDGodLgJAU/ff4DL06YR+PVXmPyqRi6Tm0UX8GqGLuDly9EvviTjDy3cziNGi5kuzXf+wtz5tNm5mhQHF+I9GqJ4JyHi3HKKXyiAkNo5hQSRkobIUjEev5okyglYvf8PiHegdfY9pK7PbWEbx9/58NcfOBmj0quOAdm1O6EXsjDHXgCrxbZgSjJ/8346nLpMojmO+JoGgt8L5vDMC+xadILXv+6HKlUMwsDF8B0cOruWFkE9sWSn8cl/b7DNoPXjZLhari0h5iIHRtyDfVp2jv1cuQ7b5/gLVeJgWMhJw59Yk5IAyDpzRhdwnVsTXcDLl+TNm/GIiSGhUSPMXl7YeXpgFxxcrD4uXrzIww8/TFRUFIqiMH78eCZNmsTbb7/NzIWz8QScGzXkw1fH8PTAgQX2FRYWxtKDzjz9v15YVTCbrQx6YjxnL2ynz8cPMGxAS6zSilW1YpVWIs+vp+uahbTxM9CttoETcSqfJr7EsBq/UuelpQAkbJlP5r7lNAz0xznbSmRcJpMnjccp1Z6ZlxeSbcrgT+tFls9vjRHIvvLZO7dE+2uABxyC6FtvII0b35Nja6u2XXCMicNiAoOjPV/c2w0phK2OqNA+w0JQ36M+bg6epGzciCUykvBnJ2DfsCFqairCTou3N7i5ac8NBoSikHniBDUmTsRjeOUPFSwIXcCrGVW5pFpVJdXbm64rlpf4eKPRyKeffkq7du1ISUmhffv29OvXD4AhDZoz2t6RPns2F6tPRVFAtfDtp69z5t8/uOtFL7azlCc3XWbmne9Q09GZ46tfovuBRfx30cqCQ2Za+ipEp0kG/5yCdcAeHOt3RKpW0o5txpqdRVRSCu+mpoCEh/sM5Y77HkPNVsmKMZM08RR1vRx5oIM/FgOoBkG2IrEYwGKA1gZJ67sfwtHpapIyg9HEt/Vrk3FHSwZ/tbTQa/J55mkufzgNNS2N1M03vh+OrVsjVRWsViyXLxM5ZQqud/bH4OIC5J1IrbKjC3g1Qx+BVz38/f3x99dSvLq6uuakeAVAtWItJO/Jvy+9TeDfv/N4lsrx6AhSszIJDAzkwXv7svTXBUgr7Po8DQOQUO8ctY8vRAHaATFpKi18Dci33MgwS/r/nM7kbna84eJEItpKUbeO95IZdoBGnoJJSdE8cfo8lkefZ7WLlomwS1wCa/zrEuNjwnBRYrBqD6MVDFaJyQJwjrB2ndk6pB5NPBrhVV8rNJHo44jz+RsLFueF0ceHWp99CkBo+w6oaWl4PfIw8fPmY/D2ps7CX3MWGx1voi1eyj59Gsc2bYC8E6lVdnQBr2bkV71Ep2pwbYrXbdu2sfLsCTYIhd5jx/L8kxOJ2HmQ7OPHyPb0IbhLW9oN7Eng378DMMdegdqBXGx1G/0XzSUpPoanvHdTT0Tke77IVMkjy9KxqqBKGNHcxOBGJn5cNIW/YgJAKBjdauB157McO7iW5Q398EhO4fiE+/Cu4UFsSirub/0P762raVqjTp7nkFKy4/+exPPPrdy+/CxwFliNJSKCqVFRCODC7NmMHz++yO+TKSiIrOPH8XrsMUy1Arn84YeEduhI3T+WYF+3LkZ/f5zatcsRb8g7Sqeyowt4NUMX8KrL9Slen376aTpsO0jdC8eZtnYDb/+xlA/8rxZj4E8InXxjP6ptMtDdqwbH699HvbNf3dAm/D9PArsn0KqmgQNPutyw/48RTsQ8u5eXF/7C5oiGqOYsMs+HoAQ1YOSYRzmZ6sxrE17io48+4oHRj+Cbj3iDdjfYddps5IeSYyHryLgUjv3/fcovQUH4Gk3EWSyMmziRgLAwBn/4Yb79SFUl5quvSF61CvP5Czi0boXR2xvPh8ZgTU4mduZMzt41EKfbumCJLNqovrKjC3g1QxfwqonZbGb48OG5UrzWrFkTx/RUFCG4s2Ygb2RnYvx+AUHtm3Nq/3Ei9oTgvWAWTum5y5Kp1/z/y//WQkDuc6WMCSF61ThU3yMERWeS1KgdwskXfJuhhm1EeDfGqfPLRF6I5NCP80iOSSA1S3J7D1c+eLofLs1GMWLECObMmUNQUBCLFy+mKAghaN62n1aBd/BjSCn5ft4keu3OoO+iP9n8ww80XL8B+3r18Hr4IZJW/IVzl854PvQQQlFI27qVuFnfAeD95JP4PPN0Tl6YGhOeJePgQdK2biV9x04ADO5VvyiILuDVDF3Aqx5SSh5//HGaNm2aK8VrZGQkDk8/x+mzYUQ6ZHLb/v00vL0DAC26t6NF93bwwljO/LeXY78socFGbSK17qHtbPz4GxSLBd8tyaT1tsO5ZjYAl/b7kvzbQByBs+5unGrigoOlHzVrdUEoCopXb4QiSIqNwdNZ8NfSWaRe2Mol67dABmmplwjy8mL9+vU3dc1paWmoqsr4R7/iXMSHbE9L5+XBg1APHyHjwAEuHTgAQOrGjVye9hGYTGDW0so22LwJU80bK84HfT+b7AsXMIeHgxDYN2lyQ5uqhi7g1QxdwEufUwt/I2nVKi0vh9Tyc2hzxRKP48dJ9fK6qf63bdvGggULaNmyJW1sPtsPP/yQhQsXEhISghCC4OBgvvvuuzyPr9+9A/W7d8Bsfp9V3QfQMOkSfj/OzNl/+GB92j9Wi7BvD2NJ1zL7ZQS2gBoA+8k0zuH85TlFsjXSfhHbXm3BE5/kvWinqFy+fJmhQ4cCkBUdzZ0uzjy4aBFqaiqZR48i7OxI37WLpL//Jvv0mRzxBjB6eubbr11QEHZBQTdlW2VCF/Bqhi7gN0eKzXfqavM1bxj7OP7bt2MymTDb2eVepCMgw8kJ1ZZtr6R07949J3roWgYWEvN9PSaTkVrz55OVmoK7qyOq0YRqMtHU25Xjr0/HlH4MAP+FS/Fo25SU5FDOnVqOydAYodqhSomUKlKVSFXVnkuJqlpBSszpkZwMSSQ7xY/M6DgcfEteBLhevXocPKjVp4ydNYuYL74kaemfeAwfhnOXLgA4tWuHz9NPA5r/O3HJEuwbNNDivUtAXonUHn/88RJfQ3kg8vpglBUdOnSQe/fuLbfz6dzIqlWrCAkJ4fXXX69oU6oc5owMjnfshMliIdvOjiwPD1yjowGotWE9bgEBhfRQObGaLZxs2RIA+ciLNHv9iRL3tfHlORxLrYspO5l7xjfEr0vzm7bPHBFB2JgxWCIicendm1pffoFSQpGuqggh9kkpO1y/Xc8cU82QUlbaVLKVHXNaGiaLhQxnJzI6dEB1dSHVyxNl6tQqK94AqacvAJDh4H1T4g3Qe4Y2YjXbubH6mwNkxiXn2p+YmMh9991HkyZNaNq0KTt27Ci0T1NAAA3WrsW5e3dSN24ktFVrzFfi4Ks5ugulmqG7UEqOtK1i5Z576PJW1avJmR+ujYMBcMyMIyshBXtP15vqz8vNQnyykTTnAOZN3oprchiNm9rT/p1xTJo0iQEDBrBkyRKys7NJT08vUp/CaCToh+8Je2AUGSEhpG7diucDD9yUnbcCuoBXM3QBLzlXCibIW+z9k1Y157nRyb6AlkVj1PT+XNwWStTBC0QcSyDc2JSdl2Hj2L/Y+O965vygTYja2dlhV0xXyJX/A2M1SVZVGLqAVzN0AS85VTENgZqVxc51G/nlYgw7PX1okZXO10Pv5M+vfsP/8CG8L57GPiGcK1UlS2tOrHa3xtTuptX9PDxzKVuOeBCXHIlicaF7i0Fk2kfRoVNHvvzyS5ydnYvUp1RVMg8fBsC1gEr11YlbQsDzS0Lz9ddfM3PmTIxGI4MGDWL69OkVaGXlQBfwm+CKuFWB9y/6XBi/bt7JHyZnTgXUxlTfB7PRyCWg0d7T0LUDXVwV3lgWQXanATg0aUSNPrdhdLj5Efj1tJwwjJbAkneOMn3pKe7v9hyNfOoQYviHjz76iPfee69I/QhFId2xBhcD+xD18wlto7QVVZO2ghnX/B8JAW4+jnS4K7jUr6myUKiACyEcgC1oBfaMwBIp5VtCiE+Au4Fs4AzwmJQysQxtzZe8ktBs3LiR5cuXc+jQIezt7Ym2RQtUd3ySDtMwZSkXZqxECgVVGJBCIVsqnMGbzS69tYZaimfgWpeB5Nps1rnGatfHz+VBvj8ctu3X782rvbimfUHbc5tzTf/i2v0i7zZXNmVmoISfR3rXQHr54JAQz1AgfdMW/omchGowII1GVIMxJ8Wplrj6Si/XbEOAIgCBzx196dazW55vxc1gzc7m383b+DU8ho21gjHXaUKThFjeNmTxQLd2RIRH8Ob2EP6rpS1rb9K4Kx02jC11O/Kj+5P34//NBwTXbEo24Bfrz44L+U9iWswWUs9Hk3w+muSIBFJjUjnc5gWy7d259F8ETm522lsMOYIN5Hx2szOtZGdYaNGjFg7Oxau1WVUoygg8C+gjpUwVQpiA/4QQq4B/gdellBYhxMfA60AemRdKB4vZSnaG5v+yUxMwGkD72ZX0aNOQsPMXQLVAkjY7/e1Xn/Hac49jnxENGeBrByRevLHjPEUlj20V0a4MRnrNzCF4cJr4DF8UVAQqilSxUzNoQhabrS1RxdUPe24LZK7t4sbN17/IZ3N+bXJvz7OVlLmqu199fk3rfL0AhbW55vokOJiztOdxMWQlJ2GxWrnk748xIxOnkAMYLRaMVgsGqxUhbSWLpcyxRcirr4VtmGiwWslevpQt//uOHt07X29AiQg7e44FOw6w1MGNSC8fXGrac19yLI90bEWbOm1y2nnUq8OSenV4fe0JfjRlsjUrgz3n4ulY9+YWGhUVPz8/6jZuiJ/fWaKi6rEj7DQ+Hg7s/fIv0uIzSE0xk5EOGRYjmTiSbXCCXBV6nMFO89e39Imkx/sFLxY6sjmczQtPYrWoBbaryhQq4FJziqXaXppsDymlXHtNs53AfaVvnkZseAq/v78HgOaOa+jlPuvGRokqxKbD51oaypM7Utkav5Ypz1pwMApm9HOgYy3DjcdVMzwAPIPxmnQw1/Ztf71Ht30zeOe5Z3F0KJpPslry5tSbOjw56jKH7huB8cVJHP3lF5o3rF+ifjIzs1ix6T8WxqSwKyAINaAeHS9f4hXSGNqvE46m/EecLbydITmTM66Cu8Mu8GFMKmM7lc/qxK+//ppx48aRdCkaJ4fa9G75KruOOwPOmMypOJCOoyEbL4csnFzTcPa0x8XXFbcAT9zq+OJSuyZKEd08ilET/2ot4ABCCAOwD2gAfCOl3HVdk7HA7/kcOx4YDxBUwiWssRdTc567N2wI13lDsrv9H2Qo8NeXcPf7gMCyYAoJ/s3Y+dlo9hw9x4jXv+Hsik+vuy3PYxiW5yRORbXLo01pjMr9Wt58Hzolws2vJnVnf0fk6NFEPPkUnr//RkCNoq9YVFWV9xavZKGjB4mu3vi6GRgfH8FDXdpRv3ebIvUxun1t3I+YGBcTBcC5rOySXEqJaNOmDXv37sWamsbRlz/EZYAXHg1r4xpcE5OzY6mey2ATcNVSfosVy5siCbiU0gq0EUJ4AH8KIVpIKY8ACCGmABbgl3yOnQ3MBm0lZkmMjL6gZVMTiqD1Q0PImvEm9iItZ/+lk8kw4HFwnAftHwUgsOFChj31GqJ9Lzq1B+WdBcQG3UWNGjVKYoKOTqlRq1kTEj/7DPtnn2X3U89wx8/zcHF0KPxANL/wzy7epDg587k5kfsH347RUPw7y/aB7jhERpJpFHxvSeW2w5F0DPaihuvV0W3U2Qj2rtxAqz5dCGrRoNjnKAiDizOtZn1Qqn3ecA6bgF8+d5HMVAXFYEBRFIRiQDFc+1dBUQzafoMReyenMrWrNClWFIqUMlEIsQkYABwRQjwCDAb6yjJck1+/bQ0ObwxHqpKQ7ek0f/UcUdv+xm/7YwDUjZlJ2NdfIaOzSHivC86TVnPvvfeyYcMGevXqxcmTJ8nOzsbHx6esTNTRKRbNe/dk++tTaPzBu6x84VXu+98XGAtZIauqKv/tP4hQtLuw80kpJRJvAH8PR071aU2TdQdJsxOMjb1MzQtR7OrVlJ1L13N82wbSE04CklO7/mbMtM+oEXQ113hidBLbF68hOiwUO0cnHFzccHJ1w8ndnYDGwTRoX/GZ/uycDGSnrmDl56eLdVxwm/YMf/2dMrKqdClKFEoNwGwTb0fgDuBjIcQAtEnLnlLKoi2nKiG1Gnky8o2O/Dv3GDv+PEPorii83NyJSRuAR4AHE37fyc6jocSnm2n5yW5eTn2HZ977mLFjx9KiRQvs7OyYN2+eHj6nU6no+tAo/j1/ntY/z2PJh5/wwBt5xwAkJCSyYON2frUaCPOpiauazZjIMMb2635T5zcZFM7c2ZYeK0M46QzO8Zf537hpSDUdxeBCYPO+BDZpxs6ls/jtzak8+tlnHN92hEPr1pJ0+RDIbITigFQtaDfhuVEMbngGtuTuiWPxDrwxvWtZk55wCdWsiXeHe8YQ0KgO0mpFtVqRqoqqqqiqFWlVUa1WVFVl40/fERayj/TkJJzc3Mvd5uJSlBG4PzDP5gdXgEVSypVCiNNooYX/2oRxp5TyqbIy1CfQlVFvdubcoVjW/3SM+AjBaZ7E7ryBb2a/SWDLABL3rMPjn/vIlH9gMn7Kzz//XFbm6Nzi5LW2YPHixbz99tscP36c3bt306HDDbmFis0dUyazOjyc1j//xF8nTmDKzsSUlIRzQgJn6zVky133sLpmbTI9A2h6OYIPUqMZ2bsbLi43P9GcmJbGnN37OOvgDBhIdDbg5luP1v3upN2A2zHYam0KxcCOxV8w+5lHQWaDMOEb3JYOdw+kSdf2AGSkppEcnUDi5Tj2/LWG6LM7QFiJO7+NX6ee4bkff7hpe4tL7Wb1cp77BDamYce2hR6zaf73SFVl97JF9Hr45vLClAdVMhuh1aIiBMRHpnPw+3n0Ff+HBQeyHvgL59+0at2Rzd7Df8TEmz5XdeFKFMquOoORihEtME8ghfaXK38RSFtcc6HbhW075LS7eozWNvfx2h2SRCAVI41ue4gG/g3L/b0A2LJlCy4uLjz88MM5An78+HEUReHJJ59kxowZpSLgAOasLFY9OwnH06fIcnbB7OaGY1YWLzz8DAnuHgyOCOOJZvXp3PbmJ59VVWXtsRP8dOo82528yLazxzs1kbtMVp5u25L6NX3zPG7t94s5d2A7jW/rQZdh/XEowurJ7IxM5r36FsnRR7F3aYWzhwlLtpG0JAue/i7YO7lSs24A+/+eDYCTe12cPX2RElSrSqMut9Ht/v43db2fjhwMwEu/ryxS+5S4WO2HCpg4fwkm+6LNTZQ1+WUjrJICfi0pF87jOrfVDdszJ5zCwSfvD6POjRw+uArP1S9hlJarsctXpFVKuPJa5sittu2a18IWl5/zWkoEak4fOcdc81rJP2ibba2eoduwaWV/8fkQFhbG4MGDc63uBejVq1epCnh+vPblbH5q1Yn6jvZs7dS4RFkkMzMz6dGjB1lZWUQlJaPe1gPD+Bcwr1+Fef4sUsMvsnPnTjp16lQGVwCZaenMffE9MhIPIxRnpJoBqGg381p43++7D3IsMhpXR2de7t8VgAU79hOTkoaHXyBpGWl4eHgQEhKCarESHxVH7IUo4i5FkXg5mszUNHxqB+Lg4oCnny+NOl/9obsi4D51buOR6VOKZPOXDw3Hkq2tAXhi5lzcalS8jtyyAg7AgZ9h+bM5L1WpIF+PxOBQOX49dYqAlLZQSq1ogHjPh+2tnqbrsI8qzKSKFnCAe/adYndyGp3dnVnervh3I1JKrTyZwUDD/44QP3Esjz39FI917YyXq2up303kR1Z6FvZO9jmpHKSUXDx2mhPb95HpaU9gvTo8/PDDHDp0iJ9efoOES4cA8ArsyMINf2MScEfThqiWFK4If354+Hfhsc/+D0VRuHzuEj+/9iQALfo+xJ3jRxZqq8Vs5qcXnyIp+jJQ9NF7WZKfgN8SuVBoMxqSwmHTNNTuryJ7vobBpC/aqVJcWYoOWKwCE6BNu1RvlrWtz+27Q9mVlMb4I2HMbhGMVVVJtqh42hX+9RVC4OKiVZV/RU3mdYuF0IxsOrdrV6554e1tWQ6vBBIIIQhq3pCg5tqPUlhYGACKotC8Z0/++1UT8LiLu9kdephJg4bj5lsXZ3cvXLx98Kjpi1dATWoE+WFnb+LU3qPER8RzeN0PJEbu5H/jJvDMDzNx9fHkymj/yPoFRRJwo8nEuK/n5IzeKzO3hoALAT0nQ8wJlP+mw5k10PEJaHk/mPRReFXDolo0AVd0AVcUhQ0dG9NhxzFWxCRycvcJTqdnYpHwZZPajPQvfBGQ1Wqlffv2nD59mjYDBhHaqTfP/bOObwbfnH+5rOg85E4un7lE9PlQsr19qXcqkncWFxyQ0GFQDwCEyOLQvwvISrvA56Puydnv4FaP+15/rcg2WC3mwhtVAm4NAQdNxO+dBXV7wtbPYMUE+OcVcPGF4O7Q5Rnwu7nahDrlg9ViWxmo3Dofz5vB3qCwuVMTOu08xom0TFwNCulWledPXMTXzkRvb7cCjzcYDISEhJCYmMjQoUNpc2gHf7S6Db8NW8vpCorPPS9qSbaefvppRo8peoHkfuNG4uzuzt5/9mNO3w5An8en0Lb/bcU6f1x4HnmTKiG31jfE5AAdHtNWY57bAqfWQvIlOLwYQn6B0UugYb+KtlKnEMxWm4AbKi6DXF4Fbr28vHjuueeIiYlh0KBBtGnThjVr1pSLPV52RvZ2acbpjEw6uLuwMS6ZBw+d5aHDZ1nfoTGNXQpfhu7h4UGvXr2wz0xGxkfwjacfrknJhR5XUVgsFpYuXcq+ffuKdVzX+wfQ9f4BRJxOpGawW86KzOJw+VzxFv9UFLeWgF9BCKjXU3sARITAnH7wy33QeBAMnQUOBY9adCqOnNvXChTwhQsX5rl96NCh5WzJVTzsjHSw0/zZvb3d+KxJbV44cZG79p1i921N8bG78f2KiYnBZDLh4eFBRkYG69atY/LkyTzdqwcD/93GHlXwxdYd1IiIJVFKvr3rDhzy6KciWLduHU2aNCEwMLBExwc08CjxuX2DS5ZkrLypHtVtA9rAC8eg2yQ4uRrmDoDECxVtlU4+qFZNwEUFCnhVYJS/NxODfElXVXrvDiXTVm7sWiIjI+nduzetWrWiY8eO9OvXj8GDB7Ph338JmzAWy7HDLHzvbf733ruscvPn4b/Xo6rlm71v1KhR3HbbbYSGhhIYGMicOVrJtd9++41Ro0aVqy1XOLj2bwAcXd0w20IKKyO3RhhhcTizAX5/CKQK/d6FNg+CnZ4+tTIRGX0O//+1YVePaXTu80xFm1PpGXfkHCtjkmji7MDGjo1zpYwwq5I0qxV3oyHPVBKq1crve/bRJDCQOSGHWeJckxHpMXw16NZzNUafPIOHvz92roUnq0pNiGfVN59x4XAIAJ7+ATz22SyELXInM81MSnwmqfGZpCZkkZqYhTVbxWpVsVpUVIvEalXJTDUTfiKBJrf50feRZiW2/daOAy8uYdtgxXMQfwZMztD4LrjzA3DVC6VWBsIjQwn8rhO7ekyjdbdHsaoWLXeF1HJXqFYrqrQir7xWVVTVov0oS4m0LSi68lxKFZBIVdqWEmlIWx0uR2dP/H3rVuAV3xxSSgbsPcnB1Ax6e7nSz9uNueGxhGdlk6lqV2sUsLZ9Y5q55u8rV1WVEcvX8J+HPy+SzKu9e5S57dmZqUSe3445OwtpsWK1CZ+iQO1GnbFz8MDOweWmzmG1WPj3oy84engTBmGklm8T/Oo1RI3Jpv5dt1GrW+t88yQd3riWtbO+QRg8EYoP9dr1A8WX8NCEXFmhFUVgsFMwGBUMBoHBpKAYFNKTs8nO0PLEPDurT4mvQRfwvLiwE/b8AIeXgL0bjFsHNRpVtFXVnvCIEwTOLp1qNUXlwridBAU2LddzFoW4bAtmKbGoKmlWlVSrlWxVYgGsqsSKxCIhW1V55tgFzNd8n50UhWypciUd9vsNAxgXWPCqwsxsMwNWbiDU3YdPXeHBTu1L/ZqsVgtH/1vE/tXLSAhPRc0u2JPrEejM2Bm/lSgZXVpkHMvfe4/IuNMEeDfCydWN8xePYLZm2lrY4ejUGWnfkRqB8TTsVJuEqGTiwlNIjrOSlW5CVV0QOZWBVDz9XPBv4EFQMy9cvBxw8bTHydUuJ0vktfy35BQH12kRLc9827vECfVu7YU8JSWoi/boOhHm9IdFD8Pja8Ch8mchu5Wp5deIHd3eRU2PRyhazU4hDKAoIAzaJLViRAihbReKbZ8CKNcsCtL+ipzalLbnkFMPTj25li7nlpGRFl9Rl5svvXef4HhaZuENr8NREezs0pSa9nZIKfHfpFVfevd0ZKEC7mBnYmn/btyxbicvKj74HQ+lT9PGJbL/eqLO7+bAuiWc2XGMrBSwc7VQq0UNajdri72TB4pBsY1cBQmXz7Nr4U6QkBieRmZ6LI7Oxcvlf3L5JjYs/oEMSwq9+j1Ku8eHI4RAtVpJj0pg1fvriFZ9kDZxjgn3IiY8DTAgpTuKkoq9UzauXin4BLri18CXeq0b4OBS9HzhHr5X2ybHZuJeo3SLVlRvAb+CfyvoNhE2fwwfBUHrUXDvt1Wi+vitiFAUbus3qVzOtd/kBOeW5a6pWUk4YRPvOg52NHK2x1Ex4GAQmITAIAQKoAiBQYCCQBGwJCqBGLOFueGxvF4/gC47j+f0N9zPs9BzZmab+ePgEaw2Ufv6xNmbEnBzdhr713/L0U2bSQjTJkc96kjaDelO+37PYl/AYCkr/TVClh3BvTY4OBW9bmfq5TjWzfiKMxf24erozfDn3iGoc5uc/YrBgEstH4xugRiSsxn0WDMC2vmwffEWhMGBgAZ+1GoaiL2jXYmv+wpbFoYC4ObjgJt36S8q1AX8Cr3/D+r3hZ+Hw8GFcHEXdH4KOj9Z0ZbplCVV4Efaz97EglZFC2t7Lqgm7Xcc5csL0fwWFc/lbM3/+n6DWoyrnf8I1qqqvL52E39a7UhxcsHdaObBjBhe7FlyV1ZsxFEWvfsqGQkCOxdJ0zua0v7O0fjWbpPvhGpaSiQ7ln/B0bXHUC3aj0jSRdj25wy6D8udL11VVbZ+8j3nTu3H2zuQ/m+8yJQJE/ll2RJA0jS4Ecs3/Yyrp0ee9gU28CBiXzRO3g4YjEZuH1VyH3V+NOrkR+iuKIQi8nSx3Cy6gF9LUGeYHAa7voW1b8CqV+HIUm1xUIv7wKC/XbcqlXEE7mpUSLao7EpK43hqBk2LsFjHy87IinYNefjwOWJt4n27p0uB4g0wd28I8+29aJwQxWPe9jzY4zbsbuLznpZ8mUXvv0JWCvQcdzdt+4zDUEh/c18fRNL5K20UFJMV1aylU6jTvOsN7WNDzrB3/18AxKVc4sCY+5m7fhMfDx/N4OdeYNL0t/lj+TIeffTRPM9Xr5s/u/dFc25rBN71PUp6qQUSukurO9r5nnqFtCwZuiJdj8EIXZ+Dy0dtI/Gd2uNP20i82/PQZ6ou5rcI8oozvBIK+AcNA3nuuLZe4fuLMXzWtGhFwVu6OnGga3MAVClRCrnLyLZa+OpyMjWtVtbd3QeT6eZcB6qqsuzLSWTEC+6c+BAtuj5QpOOE0P4PBr74GI07DEEp5Dt2cvt/AIx64SP++uZj0jLjMBkdGfrR+3jU8CY9PZ2AgIB8j/dq6oWLUXAhNJ6yyMWoqlc/Uz6BNxdJkx+6CuXH0Fna48JOCPkV9s/Ttm/7Qnu0vB+Gl3+VEZ2yoTI6UuKyr5YpczGWLLFXYeIN8NueA8S4eOCZkoBFSoqzfEpKydmD6zl1YA3+9VpitWZxbOtqLh/Loln/JkUWb4CAJk1IDDtDnWa9c4l3Rmwi0UdOE9itDTLDSkpMLGc2buPU/h04GFzw79SccR1+xJpmxv2n2QQ3rI+joyP9+/enf//8E3YJIQgIcObMhVTM6WZMTqW7cOzbZzYC4OBiwqNm2RRK1gW8MK5EqtzzFWSna0myjvyh5Vc5vFhLknXnh1XCl1qaXLx4kYcffpioqCgURWH8+PFMmjSJqVOnsnz5chRFwdfXl59++qnAUVBFkzMCL8dw2qLS2f3qAjO7MvCfXqF7vWDabdvPfo+azNi8nan9ehd6TEpiBHtWzSJ06wHS47T37ijahKnBTqVhj3rc+ejHxbKjRu36wBn++fhDDNlOpCTFkZ6eRHp2MhIVvs3d3qjY0bxpD4QiMChGktUUli9fzrlz5/Dw8OD+++/n559/ZsyYMfmes06bGpy8kMr5bZE06Jf7DufaYhgWi4X77ruPd97Rih1//fXXzJw5E6PRyKBBg5g+fXquY5NirpYJHvtJ9zKrx6sLeHGwc4L75sKgz2B2T0gIg53/08LXur8IzoWn9rxVMBqNfPrpp7Rr146UlBTat29Pv379eOWVV3jvvfcA+Oqrr3j33XeZNWtWBVubP1cEvDL+/M68EJ3z/I36ef8IhqfF8+OpLRxLV/E0KjxYux7d/a9WqMpPhEaOHEloqBYhkZiYiIeHB24fzuRbezcmpafj5nTjiFFKyemQv9m36jcijsQjrQoufpIO97elQdv+hO5eg1dgHZp3Ho2pBKub/Ru0BdZy/tQJnIxuuDp7U7NGXdx8fUmKusy5yBAAOnUfRv2OnfDr2BTFcPXOZN26ddStW5caNTR//7Bhw9i+fXuBAh58ey2UFec4fyD6BgG3t7dnw4YNuLi4YDab6d69O3fddRcZGRksX76cQ4cOYW9vT3R0dK7jkmIy+HnqTgB6Pti4zMQbdAEvGY4eMOkgWC3wVVvYMVN7vHoOihHuVJXx9/fH398fAFdXV5o2bcqlS5do1uzqcuG0tLQy/fCWLpVvBD6reR1qb9YKG0w9Fc57DXMndTJbVR7ct4eT1mBtQxYsPaHyTtJ6nmzSF9BEaMz704g1mJjSvTM9evSgTadOTHz/QzrXC+a+Ff+y46c5JLp74uCo+Wn3nr+YK3QwPSWWPau+5djmXaTHgmJSqd22Bh3uGkVw8ztz/o9r1b+5lZs1A9vRaPhZAtShtBv5/g2fnazUNAwmE0b7vH30QUFB7Ny5k/T0dBwdHVm/fn2hlYbsXO3wdTMRfjH1hn3XFsMwm82YzWaEEHz77be89tpr2NtrRSp8fa/G1qcnZ/Pz1B0AtO0fRIsetYr+BpQAXcBvBoMRntkOG96HXbPgx4Hw7M6KtqrcCQsL48CBA3TurIWcTZkyhfnz5+Pu7s7GjRsr2LqCkaX4+5JssbA4KoFs1bacP9eJrv6R122WaJONqpSo2LIA2PY7KIJMVfJ9eCyv1vXD1Xj1K/vGwZWctAYx1ec099btykfHtrM4rQFvRXqTZv2XtMvhLMrwI8ZJ+6H9bsN+4uOTGXc+HpNTCoQfRrrXJHbHVga+8TYd0yLp0aA+vZpoq5HPHdnE3n/mEX7wMqpFwdlX0v6+DnQa8CxOrvkvCBo7diwrV67E19c3pxxdfHw8I0eOJCwsjODgYBYtWoSnZ+64dKPRGXc3F4T5cp4//PYuBY/qO3fuzH333Ue7du0wGo20bduW8ePH5+xPS8zCYla5UjobVavN6uXnTNTJROJPJ+J1XQbDa4thPPvss3Tu3JmTJ0+ydetWpkyZgoODAzNmzKBjx44A7Fx2JufYtv2KNul8M1TvpfSlydu2BQmTw8Cx8AUTtwqpqan07NmTKVOmMGzYsFz7pk2bRmZmZo7fsDKye/9fdFoxhrMjl1GvaeG+34J4+NBZ1saVXX5tB0VQz9GeDu7O3O5m4okTWohaRM9WOeXRTiecou+uWCb++EHOcZmKkQ+2HyQz+jKO947EdfzVRVLZB/cRNG8mIQcOoKoqUed3EbpnFae2HSQlyopiVPFv4U77u0bSoPWQIt1RbdmyBRcXFx5++OEcAX/11Vfx8vLitdde46OPPiIhIYGPP77RR77z73uRMpvbBv9zU+/VFbJSszm9+jx7tkaQlnVjtsZr6dmvNi2G51139EoxjK+//poHHniAPn368OWXX7Jnzx5GjhzJ2bNnEUJw/mgcK7/WVr7eTO6T69GX0pc13Z7XolPWvwuDP69oa8oFs9nM8OHDGT169A3iDfDggw8yaNCgSi3gpRlGmGHVVhpOre+P3ZXajwiE0Hq3Le7PeSFsTw22VZTCtqLSIODK3kxVEpKczvbEVOLNFo6lZXIsLZP5EVfPe0W8zdmprHltIhMTc0esOKgW3uvSHMe4QH5YuYSHTx9A1q9F/0mjef3vWbgFwC/vPkBcWBLmNO1YJx+VNkNa0HHgBNw8ijeS7NGjR06NyyssX76cTZs2AfDII4/Qq1evPAXcTniRKk4U63xXyJlYj4zCkmGld9OBtK97DxYJ9orAx8OO+sFuKEaBtP3/SCFQDAKDyUDDfnXy7ftKMYzVq1cTGBjIsGHDEELQqVMnFEUhNjaWGjVqkBpf/NQHN4Mu4KVFy/s1Ad87F+r3gaZ3V7RFZYqUkscff5ymTZvy4osv5mw/deoUDRtqo5gVK1bQpEmTijKxWJSGr95g62O0vzcepoK/WvlF8SxevJi3336b48ePs3v3bjp06MAj17hRs6wqSyIjeOlULAA/NL3qyjh7eDWZ14i3g18aP21PJTrsNFnZFrr4edPF2YFFx05x6uBRPlm9lcvJqYzo2Iq4sGS8gz0IbNKM+m37UatB51Kdv7h8+XLOnIm/v/8NE39XMApPrIakovcbm4ZBlbi72HNiRyRDGj2Kc9PaJGemM33pU7Rv2IWBw7oRdHutYlfmya8YhouLCxs2bKBXr16cPHmS7OxsfHx8UFXJpl+0ieGBT7cs1rlKii7gpYVfC62Q8p7v4fcxMH4TBLStaKvKjG3btrFgwQJatmxJmzZtAPjwww+ZM2cOoaGhKIpCnTp1KnUEyrVEHPqLi5eO2UbJAsU2IleEIMFs5b8aXUlz9MmVg0SxPRdCG0XvSU4DIN1qLVTA84viadGiBUuXLuXJJ/NO4WBvUOjkbMWOLLKx58/ze2nl0p1aTu6sTsldpCQj0onJIztwYpsXdkYDi85EcdbkRO30cB519sTaphbLQy4we/VqXDxqVooJZ5PwRDVmYrVmYjDcmDtEVVWO7o0kcnckuNnR8FgiJgSZgJphxSRq4+5qx+0Dg9kc3Y5693hTt3ftEtkSGRnJI488gtWqpSweMWIEgwcPJjs7m7Fjx9KiRQvs7OyYN28eQghiL6bkHOvgXD7FSHQBL00GzYAmA2HBUJjdCx5aBvVvzq9aWenevTt5zZ8MHDiwAqwpOa7ufqgIuh2bU2C7c4EP8G79p4vUZ5ZauDsmvyiefv0KL6TQ0LMOOzs58OHRzfyZWpe/92jCbbTczgtsyGknhGDHv7v4bfdBDKokJtvM4Bou+NrZoTpBrE8jXnn3SVw9yz4Pfs2aNYmMjMTf35/IyMhckRvXYlC0iUqrNT2XgFssKof3nGfUyMEomVlYVSsDG/ei2e1jOXr5FBPWf0q2akZmCYaOfo1OLQIJORSSM7FeElq1asWBAwdyXidnpDBn+SKyM6x0GziYbncOBlVw4nwMJ+YsAlUAWijx0hn7S9UHnh+6gJc29ftAUFe4sB0W3AtT4/Rl95WYZvU7kP7SKSzZ6TkRIKrNSa3anrvO6kJdByPL2zZAlVr+bQlYpMRqixyxSkl8toUMVVLLoXhL0a+P4ikKAc41mdlpBM/GHuWV46HstdSjVtT5G9r5ubni4uLGpYQEJk6cyCNbVvPNqUv8dikBw4Y1XDpzkL49mtKwWc9i2Vxc7rnnHubNm8drr73GvHnzGDJkSM4+VVVz/PgYbD9+6lV3x7lTcZjmHMNHSpbf9xnOdk6kjWnAkAF9OXw5lC3n9uAfGMD58POMfWA6S37+kO+/n0BNX1/69OnD//73Pzp16nTT17Bxxx4yV/nku1+iogorBmkg3D0U0AW8ajJ2FSx8EEL/huMroMWNE3w6lQcn14ITPWVJK8529nT2KP18FqmpqQwfPpwvvvgCN7fiF9pu6tOclbc3R0rJ9FnbALAqBpq27cTJfTtoXacBoYv/JjExkUF9+tA+28oDHp7c0yCYQ0E1WXMklNGDxjBv8XSadiid+pOjRo1i06ZNxMbGEhgYyDvvvMNrr73GiBEjmDNnDkFBQSxevDin/Za1HZDCTBO/aahKtvbraAsYyTqfhGnOMQAyBZwL9qTP2DZYFAtOvg70vHcYyX9ZCb90kd69e3PkyHFi4y4TVKcF584dZubMmdxxxx3UqVOH4OBgfvnllxK9zwCZyWawJRoYM60zBoOWu1wxKtpzRaCi0m5BOwCmUbQ7tpuhUAEXQjgAWwB7W/slUsq3hBBewO9AMBAGjJBSJpSdqVWMK6Pug7/pAl6F2B2ymvQLuzEqBhSDCcVoTyfVjFIGBZYLi+IpDkIInnt8HN86OcPqPzm5T1tM0vf9j7FmZhI+/SPaXLzITqOR8W+9QUo9sPv1bzrXC2LO1j3888kv/MMvdH2oD53veu6mrnfhwoV5bl+/fn2e26122qTlsTgtvNEu1Z/o6YfhulrCjgiahqfTtksHzoed5dlnn2Xqmy9zW9c23HXXXaiqSlpaMq7OXiTERLB5+14+//xzOnXqxLp165g7dy6ffPJJzkrhohIZF82cmf/gGXk1GidbZFHD7cZFe+cTb7wLKkuKMgLPAvpIKVOFECbgPyHEKmAYsF5K+ZEQ4jXgNWByQR1VK44t1/66Vd48IDq5SUqJo9OykXnuc3Av3RV1+UXx3AxOdva0UexxO3qOLU2DsKtRi9C/V5Lx8XSUxCS2mbN57YMPcL/7bpr4+9Ox/7O8/85r+B+6Gra3fcEGQlb9y8CJL1Oncd9SsaswGrt/RGjSa9hl1EJVMqkVMklTHaNAcTLhMbge1Hdj1/OLmbDiHSxIfJx9mfHJDKL+u0SMUzp+/v4s/PVXbr/9duztISsrk963dwYpc344+vXrx5133llsAZ8/Z00u8Qbwcc97rcfKsytL9B6UlEIFXGozVVfWmZpsDwkMAXrZts8DNqEL+FUcPSEjARxKdrumU/4c2/kztwH7Or5C8z6TyDZnkmXOwohKB6+SRTLkR35RPFlZWTz33HPExMQwaNAg2rRpw5o1a4rcr9+H76FIyaiPvmHba68wfPQYrEIgPTwY/fwLPDB+PA899BAhISEIIQgODmbZ5i24OGXg4OTLoc2/sn3Bevat+qXcBDygzVDOrfkcg3Sm+4DNXNq2DZA4tvTBqY0v9vU9SFhykobedVjz2FziRApZqpnenz+AqwqLt6zC39WXtM0CVVWxt7fH2dGNtx9YwPSVExgyZAgHDx5k8eLFXLx4sdj22Z3XRtqW5tHUburNkD598ozYmX1oNnOOaJPh3/T95qbek6JSJB+4EMIA7AMaAN9IKXcJIWpKKSMBpJSRQog8p5WFEOOB8aDlKqg2dJsE696GbV9Cv3cr2hqdIiDjw7BgoO1dr6MoBhwcXcvsXPlF8QAMHTq02P0lpaRizchAsfUZPvoBWsQn8lO/vrSY/QPOtogXgAULFuTbj7stEZRvcPkV91YMRvxdR3He/AWxoVsxuDpgTcgi40AMGQdictrFpSdiVAyscN/LsZPHyVLNLDj2F1mWbCLSoknNCOXn+eGYzWbGjn0DR5wZ3e9tvv79Cdq3b88999yDnV3RJ5hjYxP5/dcN2Fk9yDJm8OQTQ3B2yLuoxvG448w8MJM76tzBW7e9hbt9+dTVLZKASymtQBshhAfwpxCiRVFPIKWcDcwGbSl9SYysknSdqAk4QNg2CO5WoeaUGlLC9q/g/A5QDFCnm7ZoyaN0R6jlTWZ6Il2PzyXezhMvpWS5tyuSA/0HUDMhLue1XWoa8oH76Tj1rVwZ+woi/PQmNs37GaODQsf+ZT8Bdy1BHR7h4tbZnD/zPS1GziRp5VmsiVmoqeacNtGpcUz8+33iSSExMRGTwciECRMIDQ3ljz/+4MlZr5KZkYW7uwf2EQoEQKchPrQ+0Zp9+/Zx8uRJ/v777yLZM/3dhThH1ETFlRTfCIaP65qveF9Ou8wHuz7AyeTEO13fwdWu7H74r6dYUShSykQhxCZgAHBZCOFvG337A3kvraquKAYYsQAWPQQ/DYQXT4Cbf+HHVVZUVav8fuJv+PfNq9tPrIQ1r0PHcdB8mOYyOrYCog5pq1MdPSFsK9TuDI0G5M6bHn0cwvdCm9FgydTS9VYQR3YvogNwMOguqlrk/ukNm3KJN8C2+v4889Y7RVqcI6Vk+4pp7F78H0JRuOPJx7B3Kp8R5BXsHN3wFUPYY97C/xZso5GnF88+15qkaVdzJzX1rc+/j/3IP8o+nvt0Mg0aNWTFihU5y/YTEhKoU6cO3QY9yJ+/zUJVrci/jKxY8SuqqvL+++/z1FNPFckeU7Qmwp1e8KZz4/xj83878Rsf7/kYgeDD2z8sV/GGokWh1ADMNvF2BO4APgZWAI8AH9n+Li9LQ6skze6BEfNh0cPwWROYchlMpV+ZutTJSoGoI1o12R0zIVJLzoNiBNUCTt7w4nGIP6sVf974Iez5QXtcy8nVN/bt31rrRzFppepAK5IBYLCDcevhmnzW5YXVqKUGVSpgzkKqKmdObiU29jyG5HAiIqJ4++u/SctQMJlMjB//JJMmTcozo1/k+YuIZ64ZLdvZsaO2D5l2Jj574G4mfDsfe6+CUxzvW/8tO3/djtFRZfQHn+NTq2kZX3FuDpw9zNYTh1l/wp+D0ZMByV1p6bnE+1oMoem09GlIWEQkYx8fi8lk4vvZ3xMTG0NWEmxc+BsA93Qex6HUC4wapYVHDhs2jMceewzIO2PiFWbMmMEr37zCG29/RefGzxVo+/eHv8eiWvjuju/oWuvGup1lTaHZCIUQrdAmKQ2AAiySUr4rhPAGFgFBwAXgfillfEF93dLZCAti4SgI/QeMDvDG5Yq2pnBmdoLY0Bu3NxkMl/ZDr8nQ/tHc+6KOwOUj2sRtYCcwp8GFXVCrHVzcrV2/VDXRt2SCcw0wOWpFMUxOYLZVMPFrBU9tLesrzIWqWlHe1URub7sX6HDP2+Vy3h0ndtLwjwfwMeeOvo1MUYlMlbTzN3DYrj7Df0pl2bJl/PTTTzkZ/aZNm8bx/SG8fvhQznFO3bpRe+ZM4s+eYt67r+dsH/XcqwR0zz9Xd/zlk/zx8QskXxK4+gv6Pv4s9VsOKP0Lvo6RM39iV/jVGPyazol0DDTTWAlk6HH7fI97Zvnb9Kzbib2G02za+x92dnb4+tTE1RzE8K7PAGDq4EVMWAod+wXRv+eNSaryypgIWo6acePGsWfHAZ556Q3ef2tigdew4swKpvw3BTvFjpVDV+LvUjZ32fllI9TTyZYHqhVsAsGEfeDToGLtKYi9c2HlC9oI+ZG/wKuu9ry0qw1dcclcwZwB84doI/qAttB7CtTtAcb8v8ilhdVqwfCedn0bWjxF53ve1rII5tE2P4dEXp6K6zcpwoDRaEJKyT+b59N7y6tYFCNpBifONB+Nr3dtUtMS8Kh/O6n/vE6ruH2ccG/K5E01mTBhAhMmTOD7ufMxfv8Tlv/W8ciFC/xTrx7Yu1Frxge49bsj51yqqjJn6F0kO1yN527bpBV93vkwT/tVVWXn35+yZ+kGLOkGfJs40e+xV1BMdmhTYCpSWlEUBe+arTEYS1b4ODb5EuDAqcgLjPrxakrF5U81oFWdRgghGPXeQqLSPFiAtnDqKRJww8oQQzLSksH4r15h51OLiHfMYJ2d9gO2ZdVeatjVp0fzITwwoxveLoV/bsLCwhg8eHAuAb/vvvuYOnUqvW/vz9Mv/R8fvDUpz2PTzek5Od9DokN4efPLdPbvzBe9vyjR+1IYejrZikQxaCPWfT/BzPaaP3jUb5Wves+JfzTxBnj+UNnGsF8r3qCNxu+bC/PuhogD8Mt9mrvlyS1lZ4MNg8FI1Lht+P3QjT5HZsGRskvAtaPXJyREhTLoxA+c8W6N/0O/4e8RgD+QsGwYQSd3EO/uRL24fQCcdmrLgQN/47xxO5GnT1Pj8Ue1joxG4q0WHNo9irH2baTtisHtGletoig8sXwNh+bO5t81KwAIP3MyX7sURaHr3a/Q6vYx/PjS40SfSOeXyXmnAbZzy+b+KdPwC25f5Ou2Ws18tepnvt3uTrZqj70hC21tIKyfVI/6/lcrAH317F10nL6Nnmi51a0YAAPxzh48FSRYvOAHUmrXYu2y42SrdqixZo6dPMRL9z6Bfa20Ion3taiqxJxlYc2/q6jp58fW+ZEIKVCjr/5IZVgy2B6xna3hW9kdtZuLKTeGI66/sJ50czpOpvKby9EFvLy4+0ttou/vlzXf7/9ug56vQkAb8Gtd8flSslLgN9tS6of+rJgFSO6B8Nx++LgOZCZpvvf0+HL5ofMLbMHBu+eTeHEf9gZDviF+eVGUlmpqNN1P/sJtm14B4GijETS7/2uyk0KJW/sGxtD1eMYlAhCwSnN/rPa5gxfe/5uXVRXPpb/l9GV3z4METZ6I0iAYU+1gEAJzbN6iJRAIKfEWRh745oc821yLi4c/Yz//kb1rZyOEwTYJqtjuRxRO7tpEQpgdv0x+i+b9W3LnYx8grv8xvo6zUed4fuE6Dl0OoIVvLG1rJbHrAmRkC2q5W6hb865c7c1mC5Czmj6HE8lG9hnq8s6wbgz5YDH//vwudpnxuKlOjOg2ESd7VzzqSOavWEbUVgvOKV70nliXZs3qokqV0EunWbt8Hz6enkhUTp04Q2x0El89twqZCV+unMyEgR8jLJpwB3Zz4q8zf7H+wnq2XdpGpjUTF5MLHWp2YGiDodgZtHZJWUn8fPxnXO1csUhLoe9xaaK7UCqCS/th6XiIO6W9dvKBdg9Dz8kVN8k57244t0WLHBle+Be9TIk+Ad92BWn7CjcfBj1eAZ+GUAZL2ssDq6qyb/4jOGbEYWp1P026PkrKyYW4LrwxXE9K2BjXisdXRjAiPZVHbZOQA8+eZV5QELU79sPhs6n06tWLf+/6GMXBG9deJtwHdMnVT+jvv/LPkl9wFwqjvpmDY438y6AVFdVq5si2RRzevJSoI1n4N/dg+CvfYp9PzHx0UhR9P91MttXEpJ4mnu43uNDImNSUNDq/s5I0owtCqkhR8A/E7VlWumTkn6cmxS0Guwxn7M25R8YR6ef4YdXbvDD5Pc6fOs/P8z/H4GBExUpqchJGDyP13qxHLf9a9A7qTd+gvnTw64BJKf/PoO4Dr2xYsrWR+MVdsH0mZCZCpydh4PTytyUjAT4O1p6/XfRk+mVKwnm4sBPOboKDv2rbjI7w4O9Qr2wz55UXSWuexH3Hb7m2JbTuy8alycxbsQd3g4HXfWvm7PskOhoPg4EnG7Tm9/ZtiNxziSm9n0aqFnyfaoh9vatFj8/+tZwV87/DGYVRn3+LS2DpxumrqoU1817g2OqzuNQ0cP//fYqX343lyP7vt+/5NSSA38bWpkuj/KOL8ipw8eC99/DhZ1+was0aMs1Wog0++Ax8HsVBE2t7YeXu+iZG9GnEiNkneDnRAYEgtu4pRo7pg8xWWLNmJ5mXFKwuGdSu5UfT5nVIV1Lwr1kDka4y9J6h/L39b9ZfWM/68+s5FKv51M+8cobXF77OkFZDaOHTAqWQH5GyRhfwykxGInzeArJtCeEf/xdq33z6yyJz5A9YMhbcg+CFw+V33qIgJUSGwOVjsOb/NF/5mKVQs1lFW1YqZEzzxDFLzbXt15/dGH0mnEZ29jmToy/WaUiX2+/kuT0bCI+Lw9/ow7dD3sXT0Q37eknUGD845/iLG9ax9H+fYY/ggY++wKNB3nUeS4O9675m60+rMNjBoOefo34rzR1yMOwU36zfyfrTbtwWlMjPTz9SYD+RkZFERkbmKnCxbNkywsPDtfqTi9fx7pfanWGjPvezYeo9nImP4cstO9l48Gqx42UTmtMmMLhQu0eNGsW/G/4lLi4Oo5sR33t96T6sO32D+mqPNn3Zu3cvPj75p48tT3QBr+wkR8Bn18TfdnkGOowFr/o3TviVJue3w482H+TInyt3KbhL+zVXT3Yq1OutzSHUKf/Y29Ik4sjHBCzJHRmSHmPH+fWacNT6fSn27i7YB2sj6KR/Q0heG4fMSsT7oUY4d8xduktVVWaNGEyGQWHMy1Op2bHkBQ2KStixf1n6/hdIq8B3aA+WXvblQGQN7JRs+jWIYurQYfh51iy8oyv9xcXTf9BdDBg1nJcfeZgfdx1k/cmzXNi7l8SQY3jc9Uaex9k7JLLj1aF4ORVtEvGr/V/x/eHv6V27N693er3MQgBLA13Aqwqp0bDhfdg/H5BaCJ+9C9RqDzWbg1c9aPdI3nFrJeH3h7Sc5ff/BM2Ln4Oj3Ek4Dzu+gZBfNCFv/SC0HA4N7ij82EqGzM4GBBgEmVE7cPz+6ig6IvNnMCQT8MHonG0Jy3eQ+l8GMiuOGk+1xbFZ3uGo6ye/SEjYSTylwr1vT8OrWfMyvY7V8ydz9O+jAKz0HUC8lw/DWmYwvk9f/L2Knv8oNi2FobOXc+5kNlG/vEbA49+g2F8V4+gl7+DUtAcuzbW1soM6ZjK0dX361m9drHJwJxNOMv/ofE4nnuZo3FHm9J9DJ/9yvOMtAbqAVzWSwuH0eog9qS1Ljz4OabbEPooRpkTd/ISeqsK7trSYT++oWm6JzGT4+0U4bCsMcM9MaPdQxdpUTKKmLsJi9sfR/Sz29Zyx79iaxOwN2DkFYv7uJJnWDtR8xBVT0zbE/7aJtP0SmRlDzRe7YR9ccHrbQ9/PYuPaFSgS7nzgURrdN6JMriEp7jzfP/MsAlhdox/3DQngoR4DcbJ3LvTYa1FVlfpvLsSabk/sopd5fNI4WnTvzeZTkaRkWql7bh8Xjp/izz+XkpKVRrZVxce5eKtmL6VeYv7R+fwe+juKUKjlUosxTccwskneKYQrE7qA3wokhMGXra++bninttLRq562+MWniL5OVYU/x18VP4Dxm7WQxqpG/FmYdbs2Gq/si6SuI/w1bcWpQYnHqmqRJkZDNPa+qVhSDGSl1qHm4z4kb7tI+jE7ZFY0fq/3wc6vaH7ZqJ07WD7jfdIU6NC0Nd3fev9q6bJSZN68FwlbcwlHawb2HQfw9MQnsLMr3uBCVVXqTv6L6CXvMOGRwXzy9tUMnvPmzWPWrFmsX78epyK6R64gpWRP1B4WHF/AlnBtTcHwhsOZ1G5SuWUMLA10Ab+V2PktHP1Ti2C5lmE/QKv7Cz42PV5LRnXAllK003jo+5bmpqmqHF4CfzwOBnvo/X9aKt9KUGG9MCJe/wdH/zg8JozGcjqUrL2HyTyXRVaKHxItnNShcRqZoc7IrAj83hiAybt4opMeE82KlyZwKSudIEc3hs/9uUxEPOpyHN99PAOXS4dJdg1g+Auv0Kp50SdPpZTU6nI7qVZv3vz0GV7ueScAq1ev5sUXX2Tz5s3UqFFw6btrsagWtkdsZ87hOeyP3o+XgxfDGw5nROMR+DmXfRHn0kYX8FuRrFRtCXrYVi3Fa8QBLZLk4WVgyQJXv9yLYBIvwhfXZAJ+M6FsJ0jLk+jjsPYNOL0Ohs+BlvdVtEWFEvH6Shz9E/CcmNv1IzMzydq3B6xW4jdeRs0IABFOwNv3odgXb2SblRDP5vff5nD4WQyqyviZP+JUs+AJxbxC+iZNmsTIkSMJDdVy5CQmJuLh4UFISEiuY39ZuJzzK+ZjkFY8eg3liSfGFCmd7X///cftt9+OqUYwCIGLo5Wf//ctEydOJCsrC29vLdVBly5dmDWr4JWyC08s5MNd2sSwr5Mv41qOY1jDYdgbyj4tQ1mhC/itjtUM/+sCcadzb292LzQZpE2Crp2qFVru9X/Q/flyyTNSrkipxbM3GwL3fFXR1hRKxOt/4eiXhOekMfm2Uc0Wot7/AzUrAGm+hP+UgRi9iub7DftnJX/89C0IQZCjK31fmYJX88JT+ecX0tes2dU5kpdeegl3d3fefPPNG44/ez6CedM/xi32DMne9Xj41VepHxx4Q7vrkVLy2ebNfL06TbP/o0FFus5r2Rq+lWfWawmtvuj1BbcH3p6zYrIqk5+A3yLDLx0MJnhuH0wM0QTsCseWwdIn4Ot2mni7BWrZBG818QbNbRLYAc5trmhLikjhgyfFZCTgnZHYBSaCwY+I99aTeSq8SL1HhOwDIXCwqgz5alaRxBvA39+fdu20yuqurq40bdqUS5cuXbVaShYtWpSTpvV66tUJ4K2vP8e1zwM4xl/g99cncfBI/nlYriCE4KVevXJeN3r7F9pP/5Gt504VemyWNYv3d77PM+ufoa57XVYNW0XfOn1vCfEuCF3AbzW86mo5yN9O0hJBdX4a+rwBnZ+CAR/D42sr2sKyJS1Gm+yd2VGbrK3USJBF89X7Trgbl84CoTgT8+1hkjcdLPSYLq9NpUOjFmQaFH554iGSz4cV28KwsDAOHDhA585X48m3bt1KzZo1adgwfx+3oiiMf3IM7cdPxl7N4uCBoi8Qe3+EJz7el3F3VomL9+XxH0M4E5d3GmYpJVvCtzDq71H8Hvo7jzR7hD/u/oNA18JH/LcCuoDfyvi3hrs+0vKI3PUxdHkKSrm6eqXjXpt/NPakVimoEiNyEpIWDc/hPfEcUQupZpP0dyxxC9YX2F5RFHq+9xF39BlIomrll5eeJWrnjiKfLzU1leHDh/PFF1/g5nbVbbNw4cJ8R9/XY2evjYBd3YteqWZMu67sfWUse155iMGdMsnOduKOT/9j1IL5zNh0tcBzfGY8T617imfXP0u6OZ1v+n7Dyx1fxlRF8+WUBF3AdW4tajaD1y8BAnbNglPrKtqiAij6CPwKLp2b4/diZzDHkXHUjsufLSv0mNZPPkOvnv1JNwiWzHgPqzm70GPMZjPDhw9n9OjRDBs2LGe7xWJh6dKljBxZtNjp5CQtJey1PwDFYeaw4Uy51xspTew46s3M1RZeWP86fRf3pdfvvdgRsYNJ7Sbx19C/6BGYf9GKWxVdwHVuPexdYOJ+7fmvIyqxK6VkAQTSakUYtWK/2RevirE5Lo7L27aiWq8mYk2/dInVz45n0+Y1KKpKiyYtMZgK9gtLKXn88cdp2rQpL774Yq5969ato0mTJgQGFs1FkZqs5ffx8Ch5qbr7WrbjoS5Xq+qsPbeF9jXb80ybZ1hyzxLGtRxXIRkCKwN6PnCdWxOvehDUFS5sh6QL4Blc0RbdiJDkX+PnRqyZWcR8vQJzlBsYAjA4R5LSLIt/X5hAVPgF4qQFq0HB6XOJl7cPvk5uHL1wmiyjgToePvR5+f/watyk0PNs27aNBQsW0LJlS9q0aQPAhx9+yMCBA/ntt9+K7D4BSE/RBNzdvXgCLqVk17l4Fu6+wKojUWRbtB/hGgF7+G34fOp71i9Wf7cquoDr3Lr0mqyVafu2GwyZWQlzvahFHoQnrd5J0j8XUJz8QUThPaYdEWfjWbnwd5ASd8VIA/9ghL09J8JOkZ4YR3hiHF4OTgwe9yzBvfsU2aru3bvnW9Dip59+KnI/AKnJmgvFy9uzSO3jUrNYuv8SC/dc4GxMGq4ORkZ1rM0DnYJo6u8GFD+08FZGF3CdW5d6veCBhVqlocWParVJK9ECHwHIIozApUUleV0KwuiMU1vwHHEfQgiC6vriNn8OGUIy/MOP8bSNrgcByZfCyUpIwKdZ80Ir5pQlsRGXcDI44OWWf26UjGwr/x6/zLIDl9hyMgaLKmlfx5MZ9zdgUEt/HO0KXwhUXdEFXOfWpslAGLsW5vaHrZ9pGR19mxZ+XLlQtBG4tEqE0QHFFI7XyKsjUDtXV+6e/Ca/zXiXv956jdELFuX4t91qBUKtig2ls1hVjJfPIP3q5blv+5k4loVcYs2RKNKyrfi7O/D47XUZ1jaQxn5Fj1qpzugCrnPrU7sTNBkMJ1Zqq1VfuwgOJZ9UKz2K6AO/0iSPqjB+nbvQtUtPtu7eyqY3XqPvx5+VqoU3w/6jZ3Axp+DdTMtZLqXk8KUklh2IYMXBCGJTs3B1MDK4VQD3tq1F57peKErlz2FTmdAFXOfWRwi4f55WLDk7FcqxanjBFFOs8nGFdHjhFS48fpCQc6EEL/uD+vcOLwXbbp79O7Rka526dWHN0Sg+Xn2CszFp2BkUejepwb1tatG7iS8OJt1FUlL0MEKd6oHBqOWFAYg+VqGm5KYYoYT51GVUFIWB07/EWYU1P88h7VLRltqXNdGhR0k1OPHNvmSeXLAPO4PCtGEt2TPlDr57qAN3tfTXxfsm0QVcp/rQdyo4+8KvIyE7vaKtAZSieVBsbUQ+7oWxY8cS3Lw5n+89SqYi+Ou1F3nrrbeoVasWbdq0oU2bNvzzzz+laHfhSCkRkae55FCLLadieeL2uvz5TDdGdQrC3al6xmyXBbqA61QfXP2gx8uQEgH/ToW02Ao1RyIQRRqBX1HwvAX80UcfZfXq1ZhcXOjQpDWXstO5uHUzL7zwAiEhIYSEhDBw4MDSM7wAstLTOfjvKmY9/wxOagZRDjX55YnOTBnUTI8mKQN0H7hO9aLdI3BqLeZd/5C0zR/pUgujfcpVT8Y1f3Ok9VqNzeO5BOxqu+E65oFiGiNsi3mK2jzv8VaPHj0ICwsDoPub73LxkZGsuXySgDOn82xfFsRdusieFX8QumMrlqwsvALrcKhOL14f+yCtAj3KzY7qhi7gOtULkwOM+QPL8sVk7vCDZMiy7VKUFHJUWcDVKO1rVD1nEHx1m2p1IPOooNiBb7JoN8DySiqAIlQZSjpzOmey8/tffmXFtu106NCBTz/9FE/Poi2mKQ5J0ZfZvvgXjm/dhMHORNPuvWjZpz9+9RvxWBWoilTVKVTAhRC1gfmAH6ACs6WUXwoh2gCzAAfAAjwjpdxdhrbq6JQajkPux79XFumHYskMjSfrdCJOXZvgPqhusSqcAyR9M5eUi3UKb3gN2kpHpWgjcFtuk/x84Dl9qipz33wZgLHdunPPhx/j4OXN1KlTeemll5g7d26xbCwIi9nM3r+Wsmvp7wC0GzSETkPuw8mt6tSZvBUoygjcArwkpdwvhHAF9gkh/gWmA+9IKVcJIQbaXvcqO1N1dEoXg7s9rrfXwqV7APG/HCf1v0uY/Jxx7lBwybHrERlRQEMSv1mAtEqkWSKtIC0CaVW0h2pAqkbtr7RDlY5EJJt5ackHxH73fq7SZSEhITz11FNkZmZiNBqZ+dnnBALmBF8uvrwOxTGGWu/dmI9EXpO0y5yRwp6vPqPnu9N44oknGDx48E2+W1cJP3GUtd99TUJEOI06d6Pnw+Nw8yl6vUqd0qNQAZdSRgKRtucpQojjQC20e8grqyHcgYiyMlJHpywRQuA1uinR34SQuOI0maHxuN9VF6OXQ5GON2YeBG4n9WItFJGJEGaEsCAUC8JgRShWFJMFYchEGCXCKFDsJJ6ZCXx61/t0untYTumyfv368eqrr/LWW29x11138c8///Dam1NZPPpVLJfTMcfZo6bl/bVVjEa697qT/zat4XRSAtGhh+mans6ff/5JixZFq8ZTEKrVyrZFP7N7+RLca/gy7LW3qdv2hipfOuVIsXzgQohgoC2wC3geWCOEmIEWzdI1n2PGA+MBgoKCbsJUHZ2yQwiB9wNNSFh2mozDsWDQXhcFp8nzcFSMCEPxppSudTZcW7pMCEGyLQlUUlISAQEB1HhCiyK58PxCELnD8EaNGsWmTZuIjY3lnilv0rdebUJj44lLSmVely4EBwfz3XffFcu26zFnZbLyi485u38PLXr3p/cj47BzrCwLoqovRS5qLIRwATYDH0gplwohvgI2Syn/EEKMAMZLKe8oqA+9qLFOZUdaVCKn7UJNs1Dj6dbY1ymfJfdhYWH06NGDI0eOcOnSJe68806klKiqyvbt26lTR/OxX3h+IUIxUfuzG5NybZ4ymb2nj+JhhQFPTaTWHf1LxTZzViaL33+DyFOh9B37NG36l09Ios5VbqqosRDCBPwB/CKlXGrb/Ahw5flioFNpGKqjU5EIo4LXg1qyq5Qt5bOi8frSZd9++y2ff/45Fy9e5PPPP+fxxx+/ah/ku3gz7PRxXKXg4fmLSk28AQ6sXknkyRMMnvSqLt6VjEIFXGhT8nOA41LKazPlRAA9bc/7AIWXjtbRqQI41PfALsiVzKNxXP5iH9Y0c5mdK6/SZfPmzct5fv/997N79zXBXQUEopilxMVkj8mpdFwbqmpl26Jf2L74F+q2aU/j224vlX51So+ijMC7AQ8BfYQQIbbHQOAJ4FMhxEHgQ2x+bh2dWwHv0U0xBbpgjkon6uPdZIUllfo58itdFhAQwObNmwHYsGFDgdXfr8UiJSZD6a123PrrPHb+sZCGnboy4JkXSq1fndKjKFEo/5H/73770jVHR6dyYHC3p+aEtmQcjyNx+RliZh3Crq47Do08cGjgiSnQpdjx4teTX+my77//nkmTJmGxWHBwcGD27Nn59pGVkcaZEzuxV+xINpnxsiudtXnHtmxg719LaXPnIPqOfbpU+tQpffSVmDo6BeDY1Bu7Wq7Ezj9K9rkkss8lkbzmPAA1X2qPqUbJ3RUFlS7bt29fkfp46/sH+Ns9THtxJyjWCFZ9dy+zxy7CVEjx4muRUpKUEk+2YsEalcTa2V9Tu3krej38RJH70Cl/dAHX0SkEg5sdNSe0xRKbQdr+y6RsuAhA9Dch+L3cAYNL0YXyphGAvDryT1a1rIr/5z6SmORYNmcdYa/TGXp+3xlPgxsqMuefikSKa54Dqu21WZpJs7fk9DugTj3GPz8Zg1GXiMqM/r+jo1NEjD6OuPcPxr1/MLELjpF5NI6sM4k4tfYtX0OuGbQLIaiVbGTUI28AMEFVmfhiX04FpiJcwIiCgsj5p6i2vygIyNnrYHKglkMgG7P2kCRT6X3/I/qy+CqALuA6OiXAoaEHmUfjSNkUXv4Cfg0CgXqNoiuKwswvNpaor4jUCFYvG8Idte5gYJthpWWiThmi5wPX0SkBLl0CMNZwxOBhX74nFrnDwEVxy7IVwPQ90xFCMLnT5FLrU6ds0QVcR6cEZJ5KwBKTgdHHsQLOflW0hShaSYjCOJt4lvUX1vNYi8fwc/YrhR51ygPdhaKjUwKS/jkHQNbpRNRMC4pD6X2VMs9EkL77BCiKlkJWUbRc4AqAJ3C1HJwQgkyjyu4Df9O2ZT9MxpJNqC4+uRiTYmJEoxGlcg065YM+AtfRKQGeIxoDYI5MI/Gvs6Xad+z3m0k/aCL9gIG0fQppeyBttyRtp0SYnBFYc9q6CWcSnSSPH3qNn+a9WECv+WNVraw6t4qegT3xdvQurcvQKQd0AdfRKQF2/s74/18nTH7OZByORc2yFn5QEfF5okfOc7f+zrjd4YRrH0dce9vj2tMO3+e75+z/v4e+5wd/rYhDYmZiic53OPYwcZlx3Bl8503ZrVP+6C4UHZ0SYnCzxxTgjDkqDawqUDrL2B3q10JmbULYByHTMnC/u1u+bR3dvejc/xEMc2eQLUr2IxIaHwpAG982JTpep+LQR+A6OjdBVpiWt1vYl27FdefbagGQ8l88WeejCm1vUMGKWmi7zMxMOnXqROvWrWnevDlvvfUW8VnxAHg7ejNjxgyEEMTGxt7cBeiUC7qA6+jcBIpNuM2RaaXar1v/9khLJggP4hdsKbR9UYMJ7e3t2bBhAwcPHiQkJITVq1dzZO8RnE3ORF2K4t9//9ULr1QhdAHX0bkJXG4LACB6ZgjZl1JLrV+jhyv+b+bvOikpQghcXFwALZWt2WxG2gorv/DCC0yfPv2mk3TplB+6gOvo3ATOnfxwGxAMQPTXB8g8mVBqfSv2ttJphtIVVKvVSps2bfD19aVfv350v607kbsj8fL1onXr1qV6Lp2yRRdwHZ2bxLVHIF6jtLDC2LlHSD9cOv5jNSsLAGEowte0GKt5DAYDISEhhIeHs3v3btQIlZi/Yrh/4v0ltFSnotAFXEfnJhGKwKm1L95jmmL0cSRx+WmsKdk33a/M0rIDZkdAzOxVZBw7d9N9XouHhwe9evXixNYTZMdk80CfBwgODiY8PJx27doRFVX45KlOxaILuI5OKeHYwgevkY1RU80krzt/0/0pDnZISyaKgy9ZZ12I+3Fnvm0FIGXhUSgxMTEkJiYCkJGRwbp16+jSoQsP/PYAfb7rQ1hYGIGBgezfvx8/P31JfWVHF3AdnVLEFOiCfT130nZFYUnMvKm+DK5O+L3aAWnW8o+79i+4tJpUCxfwyMhIevfuTatWrejYsSP9+vVj8ODBDKw7kBPxJzgcc/imbNYpX/SFPDo6pYgQAvfB9Yj+6gDpITG49ap9U/1lHgsDYy2E8RLu/R4o4MQQnh1daH+tWrXiwIEDN2y/p/49fHXgK3498SthYWElN1inXNFH4Do6pYzJ3xn7Rp6k/ncJqZY8V6CamU3CsjNgTsP3+QEFts00wS6fRM6f2l+ic7nYuXBvg3tZHbaay2mXS9SHTvmjC7iOTikjhMCxsSdqqpnEZadRs0u2xD1m5l8oDjVxamePycejSMd4e/iX6FwAY5qOQZUqv4f+XuI+dMoXXcB1dMoAxxY+2Df0IG13FAmLTxb7+PRDZ8i+7IG0ROA1uk+BbcPPHQKgRqoBZ5+STzwGugbSNaArK86swKqWXnIunbJDF3AdnTLA4G6Pz2MtAMg4HEvKlnAyjsRijk5HmguebFQtKnHzQkA1U+OpboWujDx0VCuhFuNipd2Preg9uz27DvxdIruHNBjC5fTL7IraVaLjdcoXfRJTR6eMEIrAsbk3GUfjcgpAXMGpnS/CpODSNQBTTedc+2Jnr0OYfLGrHYtDg8BCz3NHvyd4759M1l1Yx2aPKGIN2Rw5soHObQcV2+betXvjaufKijMr6BrQtdjH65QvuoDr6JQhXg82JftSCoqDkawziSQuPwNA+sEYsErSdmmLZfxe64TRw56M0AiywkzI9NP4PPlQvv3uPbmR0HN7cbAKZp1fSJRTNnhc3e/u5FUie+0N9vSp3YeNFzdiVa0YlNLNsqhTuugCrqNThgiDwD7IDQCTrxMutwWgZlsRJgVLTAaXP9sHQNRHu7ELdiM7LBmZnYbr7Z4IRUFKmcuFkp2dydSfRvOP/TV+dSftz91xQXT060jLNv2o27hziW3uVqsby88s52jcUVrVaFXifnTKHl3AdXTKGcVOG9WafJ2oNa07yWvOk7LpItm23OKZ+34kbfUxot56HYAG69dhqqXlBz93ak8u8Q6Ik7zb4wOa1O+Cu2fNUrGvi38XBIJtEdt0Aa/k6AKuo1OBCCFwHxCMXbAb5vAUHFs7c+m8PRkxV9uc7nsHAP4fTaPhwIGw9+q+CG/BhP1T2NPhSKnZ5OngSUPPhhyMPlhqfeqUDYVGoQghagshNgohjgshjgohJl2z7zkhRKht+/SyNVVH59bFsYkXbnfUwVTDh+CFv9L0xHGanjhO3RXLcWjWDIDI114ntFVrxl9qlOtY/wTIsmblvE7NTkXKki8gAqjvUZ8T8SewqJab6kenbCnKCNwCvCSl3C+EcAX2CSH+BWoCQ4BWUsosIYRvWRqqo1MdcWjUiLpL/yB182YuPvkUAF1/P0Z2S4GQ8GN/A+f8BB1+7lBgP76Oviy7dxmudq5FOm9Hv46sOreKA9EH6OjX8aavQ6dsKHQELqWMlFLutz1PAY4DtYCngY+klFm2fYUnYtDR0SkRLj170vTEceqv+5egJ59j4F7JHSESe1X7Cner1Y2uAV3xcsg7+iQ6I5quC7sSm5F/rnIpJanZqZxMOElYUhgARkX3slZmRHFutYQQwcAWoIXt73JgAJAJvCyl3JPHMeOB8QBBQUHtz5+/+TSbOjrVneujU/IiOTuZg9EHScxK5P/++79c+w4/omUdTMxMZPLWyWyP2H7D8QLB4rsX09ircekZrlMihBD7pJQ33GYVWcCFEC7AZuADKeVSIcQRYAMwCegI/A7UkwV02KFDB7l37978duvo6JQRUkpazc8dUWIURiwyt497YtuJLDu9jCENhnBvg3vxddI9o5WB/AS8SPdHQggT8Afwi5RyqW1zOLDUJti7hRAq4APE5NONjo5OBSGE4PAjh0nITKD3ot5YpTWXeG8ftT3HP/5EqycqykydYlKUKBQBzAGOSyk/u2bXMqCPrU0jwA4onWKAOjo6ZYKngychD4fwz9B/crY90+aZIk9u6lQuijIC7wY8BBwWQoTYtv0fMBeYa3OlZAOPFOQ+0dHRqTzUdqud4wfXqboUKuBSyv/QSu7lxZjSNUdHR0dHp6jo6WR1dHR0qii6gOvo6OhUUXQB19HR0ami6AKuo6OjU0XRBVxHR0eniqILuI6Ojk4VRRdwHR0dnSpKsZJZ3fTJhIgBKiKblQ9Vc5VoVbUbqq7tut3lT1W1vTztriOlrHH9xnIV8IpCCLE3r0QwlZ2qajdUXdt1u8ufqmp7ZbBbd6Ho6OjoVFF0AdfR0dGpolQXAZ9d0QaUkKpqN1Rd23W7y5+qanuF210tfOA6Ojo6tyLVZQSuo6Ojc8uhC7iOjo5OFeWWEnAhxP1CiKNCCFUI0eG6fa8LIU4LIUKFEHdes91OCDFbCHFSCHFCCDG8/C0vme3X7F9hK6xR7hTXbiGEkxDib9t7fVQI8VFVsNu2vb0Q4rBt31eisKrC5YAQorUQYofNrr+EEG627SYhxDzb9uNCiNcr2tZryc9u275Wtn1HbfsdKtLW6ynIdtv+ICFEqhDi5TI3Rkp5yzyApkBjYBPQ4ZrtzYCDgD1QFzgDGGz73gHetz1XAJ+qYrtt/zDgV+BIVbAbcAJ629rYAVuBuyq73bZ9u4Hb0AqcrKoIu/O4jj1AT9vzscB7tucPAr/ZnjsBYUBwRdtbBLuNwCGgte2197Wf98rwyM/2a/b/ASwGXi5rW26pEbiU8riUMjSPXUPQPsxZUspzwGmgk23fWGCa7XhVSlkhK8JKYrsQwgV4EXi//CzNTXHtllKmSyk32o7NBvYDgeVnsUZx7RZC+ANuUsodUvuWzgfuLT+L86UxsMX2/F/gyh2kBJyFEEbAEa3sYXL5m5cv+dndHzgkpTwIIKWMk1JaK8C+gsjPdoQQ9wJngaPlYcgtJeAFUAu4eM3rcKCWEMLD9vo9IcR+IcRiIUTNcreuYPK03fb8PeBTIL28jSoCBdkNgO39vxtYX35mFUp+dteyPb9+e0VzBLjH9vx+oLbt+RIgDYgELgAzpJTx5W9evuRndyNACiHW2L6Tr1aIdQWTp+1CCGdgMtpdfblQlKLGlQohxDrAL49dU6SUy/M7LI9tEu36A4FtUsoXhRAvAjPQijiXOqVpuxCiDdBASvmCECK4lEzM24DSfc+v9GkEFgJfSSnP3ryVeRhQunYXeD1lSUHXgXYH+ZUQ4k1gBdpIG7S7NCsQAHgCW4UQ68rqvc6LEtptBLoDHdEGJuuFEPuklOX6I19C298BPpdSppbX9EiVE3Ap5R0lOCycq7/woIl2BBCH9iH507Z9MfD4TRlYAKVs+21AeyFEGNr/o68QYpOUstfN2nk9pWz3FWYDp6SUX9yEaQVSynaHk9vVc/31lBlFuI7+AEKIRsAg27YHgdVSSjMQLYTYBnRAu70vF0podziw+YorUwjxD9COcr5LK6HtnYH7hBDTAQ9AFUJkSilnlpWd1cWFsgJ4QAhhL4SoCzQEdtt8mX8BvWzt+gLHKsbEfMnP9m+llAFSymC0EcvJshDvmyBPuwGEEO8D7sDzFWdevuT3fkcCKUKILrbok4eB/Ebx5YYQwtf2VwHeAGbZdl0A+ggNZ6ALcKJirLyRAuxeA7SyRSsZgZ5Usu9kfrZLKW+XUgbbvpNfAB+WpXhjO+kt8wCGov2CZwGXgTXX7JuCFlEQyjXRA0AdtAmJQ2i/8kFVxfZr9gdTcVEoxbIbbeQqgeNAiO0xrrLbbdveAc3/eQaYiW0lc0U+gEnASdvjoys2AS5od5RH0QTwlYq2tSh22/aNsdl9BJhe0bYWx/Zr2rxNOUSh6EvpdXR0dKoo1cWFoqOjo3PLoQu4jo6OThVFF3AdHR2dKoou4Do6OjpVFF3AdXR0dKoouoDr6OjoVFF0AdfR0dGpovw/x7tTgmDGCc4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ttV5yxsM2AFdbP57ZzqMlTH1rja1e3uXEhrQzOoIQDsvdXZFbUsni9Fwm940wNEtTWuAopdyBFKAb8C+t9cZfPeQO4H8NPHcGMAMgNja2RSF35pTU337uyn4oTrc6T7UmlTrd4jTfrn9AwxpooOsGdjTUoG+ond/w463z+hd60oWe06dT4IVeUQhxAbeNiCM5o5B7P0/hpmGd+X1SF6LbG9MoUk3pZqh/sLmf+1vgfq31Dsu2J4FE4CrdyIslJibq5OTkZod8+vt0Pl6Xgbub4sALU5v9fCGEsKbSyhru/CSZTYcKAZg5sQf3T+hus+MppVK01om/3t6sUSha6yJgJTDF8qK3AZcBNzVWvFtjSj/zx5Q6k+bdVQc4WVVrq0MJIUSjAnw8mfP7Edw3risAry7dS35Jpd1zNNoCV0qFATVa6yKllC+wBHgJqAVeA5K01seacrCWtsDB3A/+0P+2sDevjJ7hAfSLCqJPZCCXD+hEx0CfFr2mEEK01qfrM3hqfjoAGbOm2eQYrWmBdwJ+VkptAzYDS7XWC4C3gQBgqVJqq1LqXasm/pU+kYEseTiJD25NJKe4grmpWTy3YCcTXl1V/zFGCCHs7dYRcUS39wVgzb4mtWWtpimjULYBg86zvZtNEjXikj7hJP9lIm4K9uWXcd+XqdzzeQoLHxhFpyBfIyIJIVxcv8ggsk5UsHxXPqO7h9ntuA45E9PLww0Pdzd6dwrk/VsTqaqp48GvtmIytZE1HoUQLmVRei4Ad46Kt+txHbKAn6lrmD9/u7wvmzIKue/LVGrrTEZHEkK4qM83ZHLgWFnjD7QShy/gANckRvPQJd35aUcub63Y36QZmEIIYS3LZyYR4OPBe6sPMuHVVTz7w067HNcpCrhSigcndGfagE68tXwfl7+9ljmbj1BZU2d0NCGEC+ga5s/6JybU3//wl0N2OW6zJvK0VmuGETZFZU0d81Kz+dfP+8kuqsDH042wAG+GxXfgzlHx9JYZiEIIG8o6Uc6ol34G4LrEGF64qj/uVlg9rqFhhE5VwE/RWrP+QAErdueTU1LJ0vQ8qutMfHT7EMb17Gjz4wshXNee3FImv7EagFWPjaVzB79Wv2ZDBbxJa6E4GqUUI7uFMrJbKAA7sou56p113P7RZib2Cee1awcS4ONpcEohhDM6WlRRfzuvpMoqBbwhTtEH3ph+UUGse2I8v0/qword+Vzz7nqyTpQbHUsI4YRGdw/lb5f3AeDa99ZTUFZls2O5RAEHCPX35olLe/Px7UM4UljOxNdW8+n6DMqrZV0VIYT1eLi7cfvF8YzraZ7QM/jvy3j06zSbHMtlCvgpo7uH8eHvhhAR5MNT89NJ/PsyHvhqiyEL0QghnNdLVw+gcwfzMrPfpGTZ5BguV8ABhnXpwM+PjuWbe0YwsU84P2w7yoTXVrE/334D8IUQzq1jgA8Te4fX37fFgBGXLOCnJMaF8Ob1g1hw/yhq6kz84YsUSiprjI4lhHAS8WGnT2AeKay4wCNbxqUL+Cl9I4OYMaYre/PKGPD0Eh6Zs1VmcwohWu2v3+0AzJcxPLVioTVJAbd4ZGIP5t47An9vD+alZjPulZV8bKfZVEII53RlQhQA7m4KNytM6Pk1KeBnGNw5hK1PTeTJqb3JKCjn6R92cvW/1zEvNUsWyRJCNNu8LdmAuYFoC045kac1PNzduHtMF3blljAvNZvkzBMkZ57gkTnmYUD3JHXl0Uk98HCX9z4hRMPqzljeuk+kbZbxcMqp9NaUnFHI3NQsvtp05Kzt0xMiefP6c65zIYQQAMT9aSEAIX5epPzlEpRqeReKS62FYisV1XU8PncbP6Qdrd92x8Xx/PWy3q365QghnEtmwUmS/rESgEMvTm11fbDKVeldna+XO/+8YRBpf5tEbIh5gP6HvxzihR93UXiy2uB0Qoi24HBBeX3xfv43/WzauJMC3gJBvp6sfnwc+5+/lKhgX95fc4iLnlvKCSniQri042VVjPmHeTnZ3yd14aZhnW16PCngreDh7sbih8fwu5FxAFw3e72xgYQQhnp50e762zNGd7H58aSAt5K/twdPX9EXgL15ZRSXy0xOIVzV1P6d6m938Pe2+fGkgFvJPUldAXh58e5GHimEcFY5xfZdFE8KuJVMT4gE4IuNh1m0I9fgNEIIe6szaZ6Ytx2A9289Z8CITUgBt5LenQK5dYT5hMU9n6ewPavY4ERCCHvalVNSf7t9O/tc8UsKuBU9O70fn905FIDL317L2n3HDU4khLCXvpGBXGn5JH71u/YZ0CAF3MpGdw9jaFwIADf/Z6OsoSKEi1BK8cTU3nY9phRwG5hjuVAEwKJ06Q8XwlX4e59eXsoes9wbLeBKKR+l1CalVJpSKl0p9Yxle4hSaqlSap/l3/Y2T+tAPN3Ns6/mpWYbnEQIYS85xacv2lBT1wYKOFAFjNdaDwQSgClKqeHAn4DlWuvuwHLLfWHx43ZzyzsiyMfgJEIIe1mwLaf+tpeH7Ts4Gj2CNjt1sUhPy5cGpgOfWLZ/Alxpi4COKthyFjrAR1bsFcIVvL1iH28t3wfA53cOs8sxm/QWoZRyV0ptBfKBpVrrjUC41joHwPJvxwaeO0MplayUSj527JiVYrd9vx9jntjz3qqDBicRQtjajuxiXl26lyn9Itj61ERGdQ+1y3GbVMC11nVa6wQgGhiqlOrX1ANorWdrrRO11olhYWEtjOl4Zow5vQ7CxoMFBiaxLq017606wF2fbGbGp8n8Z+0hsousf7FWIRxFbnElT83fgZ+XB7N+O4Dgdl52O3azPt9rrYuUUiuBKUCeUqqT1jpHKdUJc+tcWLi7Kd69+SLu+TyV62ZvYOOfJxAe6Lj94SaTxs1NsWRnHi/+dHq5gCU783huwU5uGd6ZywZ0IsDHk0U7ckg/WsL0QVEE+3qy/mABg2PbM6F3x7OW1tybV8rWw0VcPTiaqloTvl7uRnxrQrTYZ+szeHbBThSK164bSKCPfSbwnNLoBR2UUmFAjaV4+wJLgJeAJKBAaz1LKfUnIERr/fiFXsvRL+jQEj9tz+HeL1IB2P3cFHw8236RKquqZVdOCdknKvhg7UF2ZJtnmHm4KWpNmhA/L9Y/MZ7MgnJSMk/w2tK9HCutatJr94sKxN3NDU83RXLmibP2ebm78e19I+kbGWT170kIWxj+wnJySyr59I6hjOlhux6GFl+RRyk1APNJSnfMXS5ztNbPKqU6AHOAWOAwcI3WuvBCr+WKBRzgrk+SWbYrD28PN/b8/VKj4zTqktdWsT+/7Jztk/qEsy2rmAcv6c4NQ2PP2rcrp4RdOSUUlddwUef2lFfXkpJxgoExwaRknmDZrjzqTJrMgnKqauvo4O+Nr6c7hwvL8fV0p6KmDjDPZlv4wGi7fJ9CtNbclCxmfp2Gl4cbPz86lqhgX5scRy6pZqA6k6brn38EYMXMJLqE+RucqGFfbMzkyW934Omu+PLu4XQOaYeHuxshftbt1zvVJXNKZU0dN32wkZTMEwyIDuLhiT0Y2bUD3h5t/xOLcD3l1bWcumZxSuYJ/vhFKiO7deC9W2yziJUUcIM9MW87X206DMDgzu35z22Jdj3Z0RRLd+Zx96fm38+GJybYfQz70aIKbnx/AxkF5YC5u2XB/dIaF8arqK5j9b5jrNyTz7oDBWRa/kZ/beezk2nnZf2hw1LA24CdR0t4av4OkjNP0DHAmwcmdKd/VBB9IwPxcDd2VYOyqlr6/W0xAJ/dOZTR3Y0ZMaS1ZuAzSyiprAVgy18n0t7KrX8hmqK4vIblu/NYnJ7Lqr3HqKwxEeDtwbAuIQyKbY+X5f9sUUU1H67NIMjXk8UPjyHI1/onMqWAtyHbsop46H9bOXjsJAAd/Ly4bkgMD0zobthJzhvf38C6AwVMT4jkzesHGZLhlH15pUx5cw11ls+olw3oxP3ju9MlzA9Pg9/ohHPLK6lkSXoui9Pz2HCwgFqTJjzQm0l9IpjcN4JhXUIM+RuUAt7GVNeaSM4sJDXzBO+vOURxRQ2/GxlXf3k2eyour2Hgs0sAyJg1ze7HP58jheUkZxaydl8Bc1OzAPDxdOPD24Ywspt9JkkI13Do+EkWp+eyaEcuW48UAdAl1I9JfSOY3DecgdHBZ52vMYIU8DasuKKGi2etoKzK3G0w996RDO5sv7XBfkg7yv1fbSEq2Jdf/jTebsdtCq01O7JL2J1bwt8X7sLH041P7xhGz4gAo6MJB7f+QAE3vL+h/n7/qCAm9w1nct8IunX0P2vOgtEaKuCyUEcbEOTrybJHkhj+4nIAfvvvddxxcTw3DY8lvoOfTd/9Nx0q5P6vtgDw18v62Ow4LaWUon90EP2jg+gZEcANszcw+Y3VjO4eyv3juzM0PsToiMJBrd1vXtpjYp9wnr6ir82GANqStMDbmGOlVby2dA//3XwErc3L0vp5ezAwOphenQKI6+DH9UNirNY6uPfzFH7akcu/bryIaQM6Nf4Egx0pLOc/aw/xdfIRTlbX8duLorkiIZIkG06iEM5ld24JH6w5xN68UrZlFfPl3cMY2bVtd8tJF4qDOVpUweq9x9ifX8bOnBL25pVyvKwaMM+I3PXclFafTDGZNF0s49MXPzTGobolSitr+Mt3O5i/9SgAL/92ANcOiTE4lWjLTr35f7YhE3eliA7x5faL47lleGejozVKCrgTOFJYzuiXf66/P75XRwZEBxHXwY/+0UF0beIEIZNJ8/CcrfXFD2DB/aPoF+V4U9gzC04y9c01nKyua/OTpIT9aa1Zf7CAD9dmsGJ3HgDXD43l8ck929w8jAuRAu5EPlx7iIXbc0j51Voib16fwPSEqAs+t6i8mhd/3M3/ko8AcNuIzjw+pRd+3o57OmT+1mwe/O9WvDzceGRiD34/pkubOgEl7K+2zsSafcf598oDbMooJNTfi+uHxHLjsFgiHbGvWwq48zlZVUtFTR0bDhYwe/VBtmUVExXsy+d3DaO61kTHAO+zJsFkF1Vw8awV9fcPvjDV8OFR1rI3r5TnF+5i1d5jTXojE87r0/UZPDU/HYCIQB/+MK4r1ybGOMRCcg2RAu7kaupMTH59NQePnzxr+7T+nZjYJ5yEmGCe/3EXS3fm8fAlPbhnbBenW2dEa03Cs0uZ2j+CF68aYHQcYYCf9+Rz+0ebAXj35sGM6xXmFH/nMozQyXm6u7Hi0bEcLihn1qJd9dfkXLg9h4XbT1+nLzLIhwcv6W5UTJtSSpEQE8wv+53nAhqiaSpr6nh+4S4+25BJ1zA/Pr59KDEh7YyOZXNSwJ1MbId2vHPTYMB8mae5qVl08PPieFk1nTu0Y0q/CIMT2lbBySoOF5Yz4dWVLH04yWm6iMT5aa35eU8+Ly/aw+7cUu4eHc9jk3vZ5YLCbYF0oQinsie3lMlvrAYwbGkCYR8FZVU89L+trNl3nJgQX565oi/je4UbHcsmpAtFuISeEQGkPzOZfk8v5uN1GYztGcbYnue93rZwQDuyi/lkXQZr9x8nt6QSgMen9OTu0V1ccqEzaYELp5RZcJKkf6zE3U2x7++XSleKAztxspovNx3mH4v3AODr6c7EPuF06+jPpL7h9IoINDih7UkLXLiUzh38GBoXwqaMQrKLKlzihJYz0Vqz8VAhX206zE87cqmuNQFwT1JX7h3b1SZrbjsiKeDCaT14SXdu+mAjU95YzctXD3SItV5cXUFZFfNSs/lq82EOHjtJgI8HNwyJ4fqhsfTu5Pwt7eaSAi6c1sXdQnn/1kTu/jSZ+75MpU4P4oqBkUbHEr9SUV3H0l15fLclm9V7j1Fr0gzu3J5XrunGtP6d8PVy/HHctiIFXDi1iX3CmXvvCH777/W88/N+ekUE0CPccRbtcla1dSbWHSjgu63ZLN6Ry8nqOjoF+XDn6HiuGhTtUAurGUkKuHB6F8W2Z1KfcJbszGPS66vZ/vQkAnykD9XetNZszy7muy1H+T7tKMfLqgjw8eCyAZFcOSiKYfEhcrK5maSAC6enlOJfN11E9yd/AsDfgRfuclSL03N5adFuDh47iZe7G+N6hXFlQhTjenV06DVKjCZ/ycIleLq7ceOwWL7ceJitR4oYFGu/S9a5sgPHynhtyV4Wbs+hV0QAL17Vn6n9OhHUTj4BWYPrjXwXLuuxST2JCvbl95+lUFFdZ3Qcp7c3r5Qb39/A6r3HuHt0PN/+4WJuGBorxduKpIALl9Hez4t7xnYlv7SKlxbtprSyxuhITqe0soYvNmZyxdtrmfT6aiprTHxx9zCenNZHRpPYgHShCJdyzeBoFu/I5eN1GeZ11G9JJLaDTPJprf35Zby36gALtuVQUVNHr4gAnpzam+mDIukY4GN0PKclU+mFyzGZNLPXHOSlRbvRGmJCfLkuMYb7xnWTK/k005HCcl5ftpfvtmTj7eHOlYMiuW5ILAOjg+RnaUUtnkqvlIoBPgUiABMwW2v9plIqAXgX8AFqgT9orTdZNbUQNuDmprgnqStXJkSxYNtRVu45xitL9lJUXsOT03pL4WmCqto63l99kH+u2A/AnaPiuSepKx38vQ1O5lqa0oVSC8zUWqcqpQKAFKXUUuBl4Bmt9U9KqamW+2NtF1UI64oI8uGu0V24c1Q8f/gilQ/WHqJnRADXJMrV7S9kc0Yh/zd3GwePnWRq/wj+Mq2PQ15n0hk0ehJTa52jtU613C4FdgFRgAZOLU4QBBw9/ysI0bYppXjnposYGB3E375P574vUjlSWG50rDants7Ey4t2c+1766mpM/HR7UN456bBUrwN1KyTmEqpOGAQsBF4CFislHoF8xvByAaeMwOYARAbG9uKqELYjlKKN68fxJPfbWfh9hw83M33hVlFdR1//DKV5bvzuS4xhr9e3kcmRLUBTR5GqJTyB+YCD2mtS4B7gYe11jHAw8B/zvc8rfVsrXWi1joxLCzMGpmFsIm4UD8++t1QQvy8mL/1KCmZJ4yO1CZUVNdx0wcbWLEnn+eu7MdLVw+Q4t1GNKmAK6U8MRfvL7TW8yybbwNO3f4aGGr9eELYl5eHG2/faG55z159wOA0bcPH6zJIPVzEP28YxC3DOxsdR5yh0QKuzKfk/wPs0lq/dsauo0CS5fZ4YJ/14wlhfyO7hnJRbDCL0/OY8sZqTpysNjqSIepMmteW7uX1ZXsZ2zOMywbIUrxtTVNa4BcDtwDjlVJbLV9TgbuBV5VSacALWPq5hXAG79w0mIHRQezOLeXil1awOaPQ6Eh299Ki3by1fB9T+kbwyjUDjY4jzkMm8ghxAct35fHU/HSyiyoYFh/CmB5hjOoWygAnn6gyLzWLR+akceuIzjw7vZ/RcVxeQxN5pIAL0Yj8kkru+jSZbVnFZ21fPjOJrmH+BqWyne1Zxfz23XUMjm3Pp3cOdcmrvbc1DRVw+c0I0YiOgT58/8dRrHx0LA+M71a//cp//UJBWZWByayvps7EY9+kEdLOi3duukiKdxsnvx0hmigu1I9HJvUkY9Y0JvcNp7Syll8OFBgdy6pmrz7I7txSnruyH+39vIyOIxohBVyIFhjV3Tyn4d8rnWeo4eGCct5cvo9p/TsxsU+40XFEE0gBF6IFbhnemS5hfkQFO89SqS8t3o27Ujx1eR+jo4gmkgIuRAus3Xecg8dOEh/qZ3QUq9ifX8bCbTncPTqe8EDneVNydlLAhWiBF37cBcDa/QVOcWWfealZuLspbh4hMy0diRRwIVrg1WvNE1t25ZTwzA87DU7TOiaT5tst2YzpHipXz3EwUsCFaIHenQLZ+OcJ9IoI4KftOZysqjU6UottOXKCnOJKfnNRtNFRRDNJAReihcIDfegbGcTJ6jpq6+w3Ic7aduaUAjA0LsTgJKK5pIAL0QqbMszjwNt5O+4V1wvLzIt1dfCXcd+ORgq4EK3g7+0JwG5LK9YRFVVU4+/tIbMuHZD8xoRohdssozYuf3stO7KLG3l023RqOSR7roskrEMKuBCtcP3QWB6f0hOAy/65ltV7jxmcqPniQ/0oq6rlWKlzreviCqSAC9FK94zpyls3mK/ic+uHm/hpe47BiZqnW0fziop788oMTiKaSwq4EK3k5qa4YmAk7948mPhQP/46fwf5pZVGx2qyflFBuCnYdMi5FuZyBVLAhbCSKf0ieP26BI6XVfPGMse5wmCQrycJMcEs25VvdBTRTFLAhbCigdFBjOjSgS83Hia7qMLoOE02PSGKnTklbD1SZHQU0QxSwIWwIqUUf7msNwDzt2YbnKbprrooCn9vDz5Zl2F0FNEMUsCFsLI+nQIZ0yOMD9dmYDI5xtC8AB9Prh4czYJtR8ktdpz+e1cnBVwIK1NKMb5nGMfLqnjyux2UVzvGOil3joqnzqT5bEOG0VFEE0kBF8IGpvTrxOjuoXy16TCPfp1mdJwmiQlpx5geYcxLzabOQT45uDop4ELYQESQDx/fPhSAH7fnMnv1ARbtyGV/finVtSaD0zXs6sHR5BRXsu7AcaOjiCbwMDqAEM7K3U0xpW8Ei9JzeeHH3fXb27fzZELvcEora7h1RBwXdws1MOXZLukdTqCPB3NTshhtue6naLukgAthQ2/fOIjt2cUE+Hiw/kABf52fzonyGpak51JVa2Jxeh4A6/40nshgX4PTgo+nO5P6RrB0Zx51Jo27mzI6krgAKeBC2JCHuxuDYtsD0K1jALeMiENrjVKKjOMnGfvKSgBGzlrBFQMj2ZZVhIe7G69fm0DfyECUMp8UtacxPcL4JiWLbVlF9dlF2yR94ELY2amCHBfqx77nL+WqQVEAfJ92lIyCcvbnl3H522vp8ucfiX/iR7JOlNs136huoSgFq/dKP3hbJy1wIQzk6e7Ga9cl8PiUXpRV1RLczpPff5ZCSuaJ+seMeulnAF69ZiCXDeyEt4dtLx4R4udFz/AAUg6faPzBwlCNFnClVAzwKRABmIDZWus3LfvuB/4I1AILtdaP2zCrEE4rIuj0xYTn3juy/vae3FJmfr2VHdklzPw6jZlfp/HtH0YS6u/NyepaIgJ98Pf2oNak8fE0F/bSyhr8vT1a1fXSIzyAdQcKqK0z4SEXemizmtICrwVmaq1TlVIBQIpSaikQDkwHBmitq5RSHW0ZVAhX1DMigAX3j+bn3fnc/vFmAH7zzrpmv054oDdLH0ki0MezSY8f3qUD36cdJTnzBMO7dGj28YR9NPrWqrXO0VqnWm6XAruAKOBeYJbWusqyT5YyE8JGxvXqSMasaax5fBwPX9IDgECf0+2vpB5hjO4eSmgD17XMK6liwNNLLnjRBq01pZU17M4t4eAx89rgHjIKpU1TzbmMklIqDlgN9LP8Ox+YAlQCj2qtN5/nOTOAGQCxsbGDMzMzW59aCNGo4ooaUg+foKi8mof/d/Zs0IxZ0wA4cbKaB/67hTX7zj1hqRT8+MBoencKtEte0TClVIrWOvGc7U0t4Eopf2AV8LzWep5SagewAngQGAL8D+iiL/CCiYmJOjk5uSX5hRCtoLUm/okfG33cY5N78nXyEa4eHM01iTGEB/o0+hxhew0V8CaNQlFKeQJzgS+01vMsm7OAeZaCvUkpZQJCAce7KKAQTk4pRcasaRSerGbI88vOWetk29OT6vvH7xvXzYiIogUa7QNX5lPZ/wF2aa1fO2PXd8B4y2N6AF6ADBwVog0L8fPiwAtT+fnRsQT5mgv2m9cnNPnkpmhbmtICvxi4BdiulNpq2fZn4EPgQ0tXSjVw24W6T4QQbUd8qB9pf5tkdAzRSo0WcK31WqChU9E3WzeOEEKIppIR+kII4aCkgAshhIOSAi6EEA5KCrgQQjgoKeBCCOGgpIALIYSDkgIuhBAOqlmLWbX6YEodA4xYzSoUx5wl6qi5wXGzS277c9Ts9szdWWt9zlWm7VrAjaKUSj7fQjBtnaPmBsfNLrntz1Gzt4Xc0oUihBAOSgq4EEI4KFcp4LONDtBCjpobHDe75LY/R81ueG6X6AMXQghn5CotcCGEcDpSwIUQwkE5VQFXSl2jlEpXSpmUUom/2veEUmq/UmqPUmryGdu9lFKzlVJ7lVK7lVK/tX/ylmU/Y//3lgtr2F1zcyul2imlFlp+1ulKqVmOkNuyfbBSartl31uWq1UZSik1UCm13pLrB6VUoGW7p1LqE8v2XUqpJ4zOeqaGclv2DbDsS7fsb1MX5rxQdsv+WKVUmVLqUZuH0Vo7zRfQG+gJrAQSz9jeB0gDvIF44ADgbtn3DPB3y203INRRslv2XwV8CexwhNxAO2Cc5TFewBrg0rae27JvEzAC8wVOfjIi93m+j81AkuX2HcBzlts3Av+13G4HZABxRudtQm4PYBsw0HK/w5l/723hq6HsZ+yfC3wNPGrrLE7VAtda79Ja7znPrumY/5irtNaHgP3AUMu+O4AXLc83aa0NmRHWkuxKKX/gEeDv9kt6tubm1lqXa61/tjy3GkgFou2X2Ky5uZVSnYBArfV6bf5f+ilwpf0SN6gnsNpyeylw6hOkBvyUUh6AL+bLHpbYP16DGso9CdimtU4D0FoXaK3rDMh3IQ1lRyl1JXAQSLdHEKcq4BcQBRw5434WEKWUCrbcf04plaqU+lopFW73dBd23uyW288BrwLl9g7VBBfKDYDl5385sNx+sRrVUO4oy+1fbzfaDuAKy+1rgBjL7W+Ak0AOcBh4RWtdaP94DWoodw9AK6UWW/5PPm5Iugs7b3allB/wf5g/1dtFUy5q3KYopZYBEefZ9aTWen5DTzvPNo35+48GftFaP6KUegR4BfNFnK3OmtmVUglAN631w0qpOCtFPH8A6/7MT72mB/AV8JbW+mDrU54ngHVzX/D7saULfR+YP0G+pZR6Cvgec0sbzJ/S6oBIoD2wRim1zFY/6/NpYW4PYBQwBHPDZLlSKkVrbdc3+RZmfwZ4XWtdZq/TIw5XwLXWl7TgaVmcfocHc9E+ChRg/iP51rL9a+DOVgW8ACtnHwEMVkplYP49dlRKrdRaj21tzl+zcu5TZgP7tNZvtCLaBVk5dxZnd/X8+vuxmSZ8H5MAlFI9gGmWbTcCi7TWNUC+UuoXIBHzx3u7aGHuLGDVqa5MpdSPwEXY+VNaC7MPA65WSr0MBAMmpVSl1vptW+V0lS6U74HrlVLeSql4oDuwydKX+QMw1vK4CcBOYyI2qKHs/9ZaR2qt4zC3WPbaoni3wnlzAyil/g4EAQ8ZF69BDf28c4BSpdRwy+iTW4GGWvF2o5TqaPnXDfgL8K5l12FgvDLzA4YDu41Jea4L5F4MDLCMVvIAkmhj/ycbyq61Hq21jrP8n3wDeMGWxRvLQZ3mC/gN5nfwKiAPWHzGvicxjyjYwxmjB4DOmE9IbMP8Lh/rKNnP2B+HcaNQmpUbc8tVA7uArZavu9p6bsv2RMz9nweAt7HMZDbyC3gQ2Gv5mnUqE+CP+RNlOuYC+JjRWZuS27LvZkvuHcDLRmdtTvYzHvM0dhiFIlPphRDCQblKF4oQQjgdKeBCCOGgpIALIYSDkgIuhBAOSgq4EEI4KCngQgjhoKSACyGEg/p/or38irMx6lEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic enacted\n",
    "enactedMAP = enactedGeom[0]\n",
    "plotPoly(enactedGeom[0])\n",
    "plt.show()\n",
    "plt.text(enactedGeom[0].centroid.x,enactedGeom[0].centroid.y,0+1)\n",
    "for t in range(1,nDistricts):\n",
    "    if t != -999 :  #change this number if there is a problematic district -- see OH FDO example\n",
    "        enactedMAP = enactedMAP.union(enactedGeom[t])\n",
    "        plotPoly(enactedGeom[t])\n",
    "        plt.text(enactedGeom[t].centroid.x,enactedGeom[t].centroid.y,t+1)\n",
    "    #plt.show()\n",
    "\n",
    "plt.show()\n",
    "plotPoly(enactedMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "df8ba3b4-0266-4ec9-a136-b4870e4591c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 0\n",
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n",
      "working on tract 1800\n",
      "working on tract 2000\n",
      "working on tract 2200\n",
      "working on tract 2400\n",
      "working on tract 2600\n",
      "working on tract 2800\n",
      "working on tract 3000\n",
      "working on tract 3200\n",
      "working on tract 3400\n",
      "working on tract 3600\n",
      "working on tract 3800\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_53/3480643848.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"working on tract\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtractPop\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m         \u001b[0mtractMAP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtractMAP\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtractGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mplotPoly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtractMAP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/shapely/geometry/base.py\u001b[0m in \u001b[0;36munion\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    696\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    697\u001b[0m         \u001b[0;34m\"\"\"Returns the union of the geometries (Shapely geometry)\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 698\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mgeom_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimpl\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'union'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    699\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    700\u001b[0m     \u001b[0;31m# Unary predicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/shapely/topology.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, this, other, *args)\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop_prepared\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m         \u001b[0mproduct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_geom\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_geom\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mproduct\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m             err = TopologicalError(\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "tractMAP = tractGeom[0]\n",
    "for t in range(nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if tractPop[t] > 0 :\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "plotPoly(tractMAP)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "561c977d-a901-46a1-8dbc-b54cea5bc503",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.610594934749987 0.709546578387001\n",
      "12.320141513136997\n"
     ]
    }
   ],
   "source": [
    "print(enactedMAP.area,tractMAP.area,\"are the enacted and tract MAP areas\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "611f8635-c6be-4e35-be05-6bbda83d23a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 0\n",
      "working on tract 500\n",
      "working on tract 1000\n",
      "working on tract 1500\n",
      "working on tract 2000\n",
      "working on tract 2500\n",
      "working on tract 3000\n",
      "finished joining DP03 to tract pop data\n"
     ]
    }
   ],
   "source": [
    "#(OPTIONAL) READ IN SOME MORE ACS DEMOGRAPHIC DATA.  SKIP FOR TX; SEE OHIO EXAMPLE\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on district 1\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 2\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 3\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 4\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 5\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 6\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 7\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 8\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 9\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 10\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 11\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 12\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 13\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 14\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 15\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 16\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 17\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 18\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 19\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 20\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 21\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 22\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 23\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 24\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 25\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 26\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 27\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 28\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 29\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 30\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 31\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 32\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 33\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 34\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 35\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 36\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 37\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "working on district 38\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on precinct 5000\n",
      "sum tractPop was 29145505 sum of our district pops was 29145504.996210057\n"
     ]
    }
   ],
   "source": [
    "#3/18/22 basic computation of district population and R/D lean of the enacted map\n",
    "# (we calculate population as a sanity check on our tractPop averaging technique)\n",
    "\n",
    "tractEDUse = [0.]* nTracts  #how much this tract is used across the entire enacted map\n",
    "precinctEDUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "\n",
    "EDvPop = [0.]*nDistricts\n",
    "EDvGOP = [0.]*nDistricts    #GOP lean of each enacted district\n",
    "EDtotGOP = [0.]*nDistricts  #how many total Trump voters in each district\n",
    "EDtotVote = [0.]*nDistricts  #how many Trump + Biden voters \"  \"\n",
    "EDvBlack = [0.]*nDistricts   #CVAP pct Black by district\n",
    "EDvHisp = [0.]*nDistricts #CVAP pct Hispanic by district\n",
    "EDarea = [0.]*nDistricts  #geographical area of each enacted district\n",
    "EDtotCVAP = [0.]*nDistricts  #total CVAP by district\n",
    "#EDworkerPop = [0.]*nDistricts  #total adult employed population in the district\n",
    "#EDpctProf = [0.]*nDistricts\n",
    "#EDpctAg = [0.]*nDistricts\n",
    "#EDpctBach = [0.]*nDistricts\n",
    "\n",
    "for d in range(nDistricts) :  #loop on each enacted district\n",
    "    print(\"working on district\",d+1)\n",
    "    EDarea[d] = enactedGeom[d].area\n",
    "    for t in range(nTracts):\n",
    "        if (t+1)%2000 == 0:\n",
    "            print(\"working on tract\",t+1)\n",
    "        overlap = tractGeom[t].intersection(enactedGeom[d]).area\n",
    "        if overlap > 0 :\n",
    "            fracArea = overlap/tractArea[t]\n",
    "            tractEDUse[t] += fracArea\n",
    "            EDvPop[d] += tractPop[t]*fracArea\n",
    "            EDtotCVAP[d] += tractCVAP[t]*fracArea\n",
    "            EDvBlack[d] += tractCBlack[t]*fracArea\n",
    "            EDvHisp[d] += tractCHisp[t]*fracArea\n",
    "            # EDpctProf[d] += totProf[t]*fracArea ... for other demographics\n",
    "                     \n",
    "            \n",
    "    EDvBlack[d]   = EDvBlack[d] / EDtotCVAP[d]  #normalize these totals by CVAP in the district\n",
    "    EDvHisp[d]    = EDvHisp[d] / EDtotCVAP[d]\n",
    "    # EDpctProf[d]  = EDpctProf[d] / EDworkerPop[d]\n",
    "    \n",
    "    for p in range(nPrecincts):\n",
    "        if (p+1)%5000 == 0:\n",
    "            print(\"working on precinct\",p+1)\n",
    "        overlap = vtdGeom[p].intersection(enactedGeom[d]).area\n",
    "        if overlap > 0 :\n",
    "            fracArea = overlap/vtdArea[p]\n",
    "            precinctEDUse[p] += fracArea\n",
    "            EDtotVote[d] += fracArea*(vtdTrump[p]+vtdBiden[p])\n",
    "            EDtotGOP[d] += fracArea*vtdTrump[p]\n",
    "    EDvGOP[d] = EDtotGOP[d] / EDtotVote[d]\n",
    "\n",
    "statePop = np.sum(tractPop)\n",
    "stateEDPop = np.sum(EDvPop)\n",
    "print(\"sum tractPop was\",statePop,\"sum of our district pops was\",stateEDPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f93187a1-7f19-4da2-a845-8e564da519fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 760019.906 0.732 0.088 0.196 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "2 764562.939 0.615 0.202 0.114 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "3 758028.598 0.573 0.104 0.104 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "4 782946.008 0.632 0.096 0.093 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "5 766275.572 0.614 0.174 0.15 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "6 772634.72 0.621 0.212 0.154 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "7 765326.174 0.349 0.212 0.203 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "8 762015.436 0.637 0.218 0.133 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "9 764158.112 0.23 0.25 0.47 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "10 769401.082 0.596 0.171 0.118 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "11 764780.656 0.706 0.325 0.114 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "12 763358.007 0.592 0.179 0.107 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "13 760073.007 0.732 0.2 0.069 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "14 771921.026 0.645 0.179 0.172 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "15 772686.099 0.515 0.748 0.016 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "16 735105.17 0.32 0.791 0.035 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "17 773585.923 0.615 0.177 0.163 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "18 765416.638 0.254 0.294 0.4 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "19 767378.873 0.735 0.326 0.065 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "20 754364.205 0.332 0.684 0.06 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "21 772025.467 0.6 0.266 0.042 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "22 764199.513 0.58 0.236 0.119 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "23 814680.057 0.535 0.586 0.043 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "24 781345.631 0.56 0.12 0.076 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "25 766672.18 0.658 0.153 0.117 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "26 775695.757 0.594 0.131 0.092 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "27 768104.761 0.614 0.492 0.049 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "28 768495.397 0.464 0.696 0.057 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "29 759272.806 0.314 0.639 0.185 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "30 767048.92 0.213 0.217 0.488 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "31 764011.86 0.602 0.18 0.084 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "32 758555.756 0.333 0.211 0.232 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "33 756608.475 0.246 0.422 0.266 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "34 761412.924 0.422 0.866 0.006 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "35 751290.327 0.27 0.476 0.142 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "36 773431.141 0.66 0.214 0.133 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "37 768150.865 0.231 0.221 0.068 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n",
      "38 780465.012 0.592 0.188 0.108 = districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\n"
     ]
    }
   ],
   "source": [
    "for d in range(nDistricts):\n",
    "    print(d+1,r3(EDvPop[d]),r3(EDvGOP[d]),r3(EDvHisp[d]),r3(EDvBlack[d]), \n",
    "          \"= districtNo, Pop, pctGOP, CVAP pctHisp, CVAP pctBlack\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "67330128-5329-4e14-a59b-1f868a0368e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a scatter plot of district-wide ACS stats vs. partisan lean\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a scatter plot of district-wide ACS stats vs. partisan lean\")\n",
    "plt.scatter(EDvGOP, EDvHisp, label=\"pctHisp\")\n",
    "plt.scatter(EDvGOP, EDvBlack,label=\"pctBlack\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dc0b1520-7419-4d9a-983e-67dddf0efdeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 0\n",
      "working on tract 500\n",
      "working on tract 1000\n",
      "working on tract 1500\n",
      "working on tract 2000\n",
      "working on tract 2500\n",
      "working on tract 3000\n",
      "working on tract 3500\n",
      "working on tract 4000\n",
      "working on tract 4500\n",
      "working on tract 5000\n",
      "working on tract 5500\n",
      "working on tract 6000\n",
      "working on tract 6500\n",
      "All 6896 home districts built for  TX\n"
     ]
    }
   ],
   "source": [
    "#THIS BLOCK -- PULL IN THE HD OUTPUT FILE, recreate the HD Polygons\n",
    "infilename = \"TX37HD1tol0.005nW424MarnoElP.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "tractPop3 = df2['tractPop']  #these four may include shifting demography out of corners - avoid overwrite for now\n",
    "#tractVAP = df2['tractVAP']   #OOPS!  I forgot to write tractVAP to all my state HD output files!!\n",
    "#                             Need to go back and add tractVAP after clipPoly slicing operation\n",
    "#tractHisp = df2['tractHisp'] #we did not use CVAP for these\n",
    "#tractBlack = df2['tractBlack']\n",
    "# ... and now the true outputs\n",
    "HDvPop = df2['HD-pop']\n",
    "#HDvHisp = df2['HDvHisp']  #we did not use CVAP for these\n",
    "#HDvBlack = df2['HDvBlack']\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "HDangle0 = df2['HDangle0']\n",
    "HDangle1 = df2['HDangle1']\n",
    "HDangle2 = df2['HDangle2']\n",
    "HDangle3 = df2['HDangle3']\n",
    "HDradius0 = df2['HDradius0']\n",
    "HDradius1 = df2['HDradius1']\n",
    "HDradius2 = df2['HDradius2']\n",
    "HDradius3 = df2['HDradius3']\n",
    "startAngle = df2['startAngle']\n",
    "\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractNo = df2['tractNo']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "#nTracts = len(tractPop)   #nTracts should be already known from an above code block\n",
    "\n",
    "#Now, recreate each Home District geometry\n",
    "dummyPoly = Polygon([ (0,0),(1,0),(1,1),(1,0)])\n",
    "dummyPoint = Point(0,0)\n",
    "HDPoly = [dummyPoly]*nTracts\n",
    "nWedges = 4\n",
    "Pt = [Point(0,0)]*(nWedges*2)\n",
    "for t in range(nTracts):  #Don't worry, we'll build trivial zero-area HD's for skipped tracts\n",
    "    if t%500 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    HDangle = [ HDangle0[t],HDangle1[t],HDangle2[t],HDangle3[t] ]\n",
    "    HDradius = [ HDradius0[t], HDradius1[t], HDradius2[t], HDradius3[t] ]\n",
    "    CP = Point( tractCPx[t],tractCPy[t] )\n",
    "    for nW in range(nWedges):\n",
    "        ccwAngle = HDangle[nW]  #these two are the angles at start and end of nth wedge\n",
    "        cwAngle = HDangle[ int((nW+1)%4) ]\n",
    "        Pt[nW*2] = Point(tractCPx[t]+HDradius[nW]*math.cos(ccwAngle),\n",
    "                         tractCPy[t]+HDradius[nW]*math.sin(ccwAngle) )\n",
    "        Pt[nW*2+1] = Point(tractCPx[t]+HDradius[nW]*math.cos(cwAngle),\n",
    "                           tractCPy[t]+HDradius[nW]*math.sin( cwAngle) )\n",
    "        \n",
    "    HDPoly[t] = Polygon([Pt[0],Pt[1],Pt[2],Pt[3],Pt[4],Pt[5],Pt[6],Pt[7] ])\n",
    "print(\"All\",nTracts,\"home districts built for \",STATE)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bc3648eb-b228-4625-bb2e-b62b0c0733fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now I will calculate Hispanic and Black CVAP percentages in each Home District\n",
      "working on HD tract 0\n",
      "working on HD tract 200\n",
      "working on HD tract 400\n",
      "working on HD tract 600\n",
      "working on HD tract 800\n",
      "working on HD tract 1000\n",
      "working on HD tract 1200\n",
      "working on HD tract 1400\n",
      "working on HD tract 1600\n",
      "working on HD tract 1800\n",
      "working on HD tract 2000\n",
      "working on HD tract 2200\n",
      "working on HD tract 2400\n",
      "working on HD tract 2600\n",
      "working on HD tract 2800\n",
      "working on HD tract 3000\n",
      "working on HD tract 3200\n",
      "working on HD tract 3400\n",
      "working on HD tract 3600\n",
      "working on HD tract 3800\n",
      "working on HD tract 4000\n",
      "working on HD tract 4200\n",
      "working on HD tract 4400\n",
      "working on HD tract 4600\n",
      "working on HD tract 4800\n",
      "working on HD tract 5000\n",
      "working on HD tract 5200\n",
      "working on HD tract 5400\n",
      "working on HD tract 5600\n",
      "working on HD tract 5800\n",
      "working on HD tract 6000\n",
      "working on HD tract 6200\n",
      "working on HD tract 6400\n",
      "working on HD tract 6600\n",
      "working on HD tract 6800\n"
     ]
    }
   ],
   "source": [
    "#Now we can compute the intensive demographic variables for each Home District, as we did for enacted Districts\n",
    "HD_CVAP = [0.]*nTracts\n",
    "HDpctHisp = [0.]*nTracts\n",
    "HDpctBlack = [0.]*nTracts\n",
    "print(\"Now I will calculate Hispanic and Black CVAP percentages in each Home District\")\n",
    "for t in range(nTracts) :  #loop on each Home District\n",
    "    if t%200 == 0:\n",
    "        print(\"working on HD tract\",t)\n",
    "    HD_CVAP[t] = 0.\n",
    "    if (HDPoly[t].intersection(enactedMAP)).area > 0.0001:  #skipping inconsequential tracts\n",
    "        for tt in range(nTracts):\n",
    "            overlapArea = tractGeom[tt].intersection(HDPoly[t]).area \n",
    "            if overlapArea > 0 :\n",
    "                fracArea = overlapArea / tractArea[tt]\n",
    "                HD_CVAP[t] += fracArea* tractCVAP[tt]\n",
    "                HDpctHisp[t] += fracArea * tractCHisp[tt]\n",
    "                HDpctBlack[t] += fracArea * tractCBlack[tt]\n",
    "        HDpctHisp[t] = HDpctHisp[t] / HD_CVAP[t]   #normalizing these percentages\n",
    "        HDpctBlack[t] =   HDpctBlack[t]   / HD_CVAP[t]\n",
    "#started 10:41, end 11:51pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "412480e7-749a-4e8c-8cd3-6c89e991f824",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing expected R-D lean and CVAP stats for tract 0\n",
      "computing expected R-D lean and CVAP stats for tract 300\n",
      "computing expected R-D lean and CVAP stats for tract 600\n",
      "computing expected R-D lean and CVAP stats for tract 900\n",
      "computing expected R-D lean and CVAP stats for tract 1200\n",
      "computing expected R-D lean and CVAP stats for tract 1500\n",
      "computing expected R-D lean and CVAP stats for tract 1800\n",
      "computing expected R-D lean and CVAP stats for tract 2100\n",
      "computing expected R-D lean and CVAP stats for tract 2400\n",
      "computing expected R-D lean and CVAP stats for tract 2700\n",
      "computing expected R-D lean and CVAP stats for tract 3000\n",
      "computing expected R-D lean and CVAP stats for tract 3300\n",
      "computing expected R-D lean and CVAP stats for tract 3600\n",
      "computing expected R-D lean and CVAP stats for tract 3900\n",
      "computing expected R-D lean and CVAP stats for tract 4200\n",
      "computing expected R-D lean and CVAP stats for tract 4500\n",
      "computing expected R-D lean and CVAP stats for tract 4800\n",
      "computing expected R-D lean and CVAP stats for tract 5100\n",
      "computing expected R-D lean and CVAP stats for tract 5400\n",
      "computing expected R-D lean and CVAP stats for tract 5700\n",
      "computing expected R-D lean and CVAP stats for tract 6000\n",
      "computing expected R-D lean and CVAP stats for tract 6300\n",
      "computing expected R-D lean and CVAP stats for tract 6600\n",
      "All done computing avg and std dev of Home-District-expected intensive quantities for each tract\n"
     ]
    }
   ],
   "source": [
    "#7/8/22 - This block: compute the average and std dev in each tract's expected values for the above quantities\n",
    "# To do this, we loop through all Home Districts to find intersections with the tract of interest ...\n",
    "#about 30min for 3200 tracts\n",
    "vGOPavg = [0.01]*nTracts   #the most likely R-D lean for a given tract based on the HDs it's drawn into\n",
    "vGOPvar = [0.]*nTracts\n",
    "vGOPsd = [0.]*nTracts\n",
    "pctHispAvg = [0.]*nTracts\n",
    "pctHispVar = [0.]*nTracts\n",
    "pctHispSD = [0.]*nTracts\n",
    "pctBlackAvg = [0.]*nTracts\n",
    "pctBlackVar = [0.]*nTracts\n",
    "pctBlackSD = [0.]*nTracts\n",
    "\n",
    "for t in range(nTracts):\n",
    "    weight = [0.]*nTracts\n",
    "    if t%300 == 0:\n",
    "        print(\"computing expected R-D lean and CVAP stats for tract\",t)\n",
    "    if tractUse[t] > 0.1 :  #skip unused tracts\n",
    "        sumWeight = 0.\n",
    "        for tt in range(nTracts):\n",
    "            intersxnArea = tractGeom[t].intersection(HDPoly[tt]).area\n",
    "            if intersxnArea > 0:\n",
    "                weight[tt] = intersxnArea/tractArea[t] * tractPop[tt]/avgDistrictPop #latter term is HDweight[tt]\n",
    "                sumWeight += weight[tt]\n",
    "                vGOPavg[t] += weight[tt] * HDvGOP[tt]\n",
    "                pctHispAvg[t] += weight[tt] * HDpctHisp[tt]\n",
    "                pctBlackAvg[t] += weight[tt] * HDpctBlack[tt]\n",
    "        vGOPavg[t] = vGOPavg[t] / sumWeight\n",
    "        pctHispAvg[t] = pctHispAvg[t] / sumWeight\n",
    "        pctBlackAvg[t]   = pctBlackAvg[t]   / sumWeight        \n",
    "        \n",
    "        for tt in range(nTracts):\n",
    "            weight[tt] = weight[tt] / sumWeight  #renormalizing\n",
    "            vGOPvar[t]    += weight[tt] * (HDvGOP[tt] -    vGOPavg[t]   )**2\n",
    "            pctHispVar[t] += weight[tt] * (HDpctHisp[tt] - pctHispAvg[t])**2\n",
    "            pctBlackVar[t]   += weight[tt] * (HDpctBlack[tt]   - pctBlackAvg[t]  )**2\n",
    "        \n",
    "        vGOPsd[t]     = vGOPvar[t]**0.5\n",
    "        pctHispSD[t]  = pctHispVar[t]**0.5\n",
    "        pctBlackSD[t]    = pctBlackVar[t]**0.5\n",
    "print(\"All done computing avg and std dev of Home-District-expected intensive quantities for each tract\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4ef45acc-392f-4223-a1c1-cdb0e22330e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "blue = histogram of each tract*s Home District pct GOP\n",
      "orange is the expected pct GOP that each tract expects to be pulled into\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_bins=50\n",
    "print(\"blue = histogram of each tract*s Home District pct GOP\") \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight,label=\"Home District lean\")\n",
    "print(\"orange is the expected pct GOP that each tract expects to be pulled into\")\n",
    "ax.hist(vGOPavg,bins=n_bins,weights=HDweight,label=\"expected district lean\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "aaa24d85-1a5d-4a09-bd5b-e64b40a7850b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "these are histograms of absolute std devns\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_bins=50\n",
    "print(\"these are histograms of absolute std devns\") \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(vGOPsd, bins=n_bins,weights=HDweight,label=\"sd in expected district lean\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(pctHispSD, bins=n_bins,weights=HDweight,label=\"sd in pct Hispanic\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(pctBlackSD, bins=n_bins,weights=HDweight,label=\"sd in pct Black\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0ba10a12-66d1-462c-932b-2d316c5e76ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on relative variance for tract 0\n",
      "working on relative variance for tract 1000\n",
      "working on relative variance for tract 2000\n",
      "working on relative variance for tract 3000\n",
      "working on relative variance for tract 4000\n",
      "working on relative variance for tract 5000\n",
      "working on relative variance for tract 6000\n"
     ]
    }
   ],
   "source": [
    "#Now we can compute each tract in enacted map's relative variance from expected values for properties x,y,z\n",
    "relDevnGOP = [0.]*nTracts\n",
    "relDevnPctHisp = [0.]*nTracts\n",
    "relDevnPctBlack = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on relative variance for tract\",t)\n",
    "    if tractUse[t] > 0.1:\n",
    "        for d in range(nDistricts):        \n",
    "            fracOverlap = tractGeom[t].intersection(enactedGeom[d]).area / tractArea[t]\n",
    "            if fracOverlap > 0:\n",
    "                relDevnGOP[t]     += fracOverlap * (EDvGOP[d] - vGOPavg[t]) / vGOPsd[t]            \n",
    "                relDevnPctHisp[t] += fracOverlap * (EDvHisp[d] -  pctHispAvg[t]) / pctHispSD[t]\n",
    "                relDevnPctBlack[t]+= fracOverlap * (EDvBlack[d] - pctBlackAvg[t]) /pctBlackSD[t]     \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a1833c19-fe1a-4fb0-b534-7351b4e125cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now write the enacted map relative demography deviations from HD-expected\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now write the enacted map relative demography deviations from HD-expected to a file\")\n",
    "#tractNo = [0]*nTracts    #already defined when we pulled in HD shape data\n",
    "#for t in range(nTracts):\n",
    "#    tractNo[t] = t\n",
    "\n",
    "EDvHsp = [-999]*nTracts #lazy way of adding these columns of different lengths to same file\n",
    "EDvBlk = [-999]*nTracts\n",
    "EDvGOP_ = [-999]*nTracts\n",
    "\n",
    "for d in range(nDistricts):\n",
    "    EDvHsp[d] = EDvHisp[d]\n",
    "    EDvBlk[d] = EDvBlack[d]\n",
    "    EDvGOP_[d] = EDvGOP[d]\n",
    "\n",
    "df = pd.DataFrame( {\"tractNo\":tractNo,\"tractPop\":tractPop,\"tractCVAP\":tractCVAP,\n",
    "                    \"tractCHisp\":tractCHisp,\"tractCBlack\":tractCBlack,\"HDvGOP\":HDvGOP,\n",
    "                    \"seatPctGOP\":EDvGOP_ ,\"vGOPavg\":vGOPavg, \"vGOPsd\":vGOPsd,\"relDevnGOP\":relDevnGOP,\n",
    "                    \"HDpctHisp\":HDpctHisp,\"seatPctHisp\":EDvHsp,\"pctHispAvg\":pctHispAvg,\n",
    "                    \"pctHispSD\":pctHispSD,\"relDevnHisp\":relDevnPctHisp,\n",
    "                    \"HDpctBlack\":HDpctBlack,\"seatPctBlack\":EDvBlk,\"pctBlackAvg\":pctBlackAvg,\n",
    "                    \"pctBlackSD\":pctBlackSD,\"relDevnBlack\":relDevnPctBlack,\n",
    "                    \"HDangle0\":HDangle0,\"HDangle1\":HDangle1,\"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\n",
    "                    \"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\"HDradius2\":HDradius2,\n",
    "                    \"HDradius3\":HDradius3, \"tractUse\":tractUse } ) \n",
    "\n",
    "outname = STATE+\"enacted_vs_HD.csv\"\n",
    "outpath = \"enacted_analysis/\"+outname\n",
    "df.to_csv(outpath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "607acb32-41ac-4cbe-9c11-8bc13c17e0c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#define a simple Gaussian as a comparison to the observed relative deviations\n",
    "normalDevn = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    normalDevn[t] = np.random.normal(0.,1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "efe1d38b-96a9-4efd-8104-1099c53a4efc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now calculate and plot the absolute deviations, similar to deFord AADP but signed\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now calculate and plot the absolute deviations, similar to deFord AADP but signed\")\n",
    "devn_vGOP = [0.]*nTracts\n",
    "devnPctHisp = [0.]*nTracts\n",
    "devnPctBlack = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    devn_vGOP[t] = relDevnGOP[t] * vGOPsd[t]\n",
    "    devnPctHisp[t] = relDevnPctHisp[t] * pctHispSD[t]\n",
    "    devnPctBlack[t] = relDevnPctBlack[t] * pctBlackSD[t]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "31bd6aed-902b-4319-a363-34d3755f50ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a plot of enacted's relative deviation from HD-expected GOP lean, vs that HD-expected lean\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... and now absolute devn\n"
     ]
    },
    {
     "data": {
      "image/png": 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5JqFseiaLbRPHKklUADzXubtJmNkEXcY4ANgnPEZHMhjUjHoHB4zKexTVlLVZVCg4Ds4sKOx/6SwTz7oIAzZRh9Ht4S0q5ZpgZN+mNb9g1mQPe2UY90LdlWtmASv6S0G7kUIzuQWz6uvH77odRrL28XZ95ObKv5s9ZT2/YGLs+TkG7S4RScAWkU+IyHkR+b6IjLt8f4OIfEtErovIv4/iOYl60fRMVttG0u3kr8se3v/SWd/7TB49j4fuGNIeS7dUUVMoTXdvu20VPrByed1tOq3X35qt2Ds1CyNR+2hTf/cWRr74CtaNH8HV64uuQT1KZkHhs4dmXbegUWdpOGCLSBLAHwH4JIAPA3hQRD7suNsVAP8rgP+90ecj6mWTR8+7FtwQwHW9UjflOr9gBsowPjC6EQ9vHaoE56QItt22Cpl0quvqlJ94/UpllqEe9iQy63EWXLbImUWF+QUTCqX1Z7OgGp7J8PvxogL3Z3eBKPZhfxTA95VSPwAAEfkagE8D+I51B6XUuwDeFZFdETwfUc/SBVcF90YQXmUwrT26fpW0DoxuxIHRUhKVs4pXL0ompGa92Co287mv1/faKFXqDlYEah5bexwiKCpVKX36wulsoOe2Zk9OvXkFX331LRRU6YIh1ZdA3iwGqpLGqmrtEcWUeAbAW7av3y7fRkQR06156jK6vbKEreC/fcNq1++73R5FVnOnEgAPbx3Ck/dvqkkoy5tFfHZqtqG65WZRoRgwWAOlnIU3JnbhxPgOHBjdiCfu2Rh4Ot8ql2rNkihVmg0IMgpnVbX2iSJgu31G6p4rE5FHROSUiJy6fPlyA4dFFD/ObOywJ7mwW7VGRzJVWeJ2VvA/fs7978zt9l4txJEUwUNbh3DktXewZ2oW844EMwCIoj5cmBOnAqo+Q6MjGRzcPRzBUXh3A2P3sPaJImC/DeAW29cfBHCp3gdTSj2tlNqilNqyerX7lT9RJ4piZGLfIy0ojaztW7nc7Lv7ds8grwvC2Vy+5qKiVwtxFJTCMycvugbqVtCNnJ2fIa/tZJag29jCVk/r1Yu5VopiDfvbAD4kIusAZAH8MoBfieBxibqKbmTy6KE57J2aDbwWODqSCbVeaN1Xt+botc7trJO9fcPqSm1rao2UkcS9mzM4fu6y6/uUNwvYd/hs5f1dmTJgJAWmrQe41R3MqkP+6KE536RBr0p5QbqHUfQaDthKqUUR+U0ARwEkAfyJUuqsiPxG+ftfFpEPADgF4McBFEVkD4APK6V+1OjzE3UK3QhE10QiiKDJP15Bfmznes9EMvt05wunswzWLbbcSODZkxc9A2IubyKXNyv/NhKCwQEDuQUT6QEDSgHv5ZdmB7b+zCBOvH7F83l1yyxun5cgFfSocZF061JKvQzgZcdtX7b9+/9DaaqcqGd5jWQtYTosufVdDhvw7fedPHpee3yXcnnPCmvdtsUrTqxpeK8+2k5mUWGgvw+7PnJz1YyI9RlZ1lf/aqjfjA01DyudEbVI0KYaQdcCo0z+GR3J4MT4Dm22+ZpyG0g3RaXw1O5h1yIhFK0wl0VWJrjzZ/JmoTIa9+L1ORodyWBs5/rK52Ly6HlmibcAAzZRizgTxnSVwoKuBXoli9V78tRdVHgVFFmTTpUaWdy/SZuR3kvSKQMDmprtncTrwpFbu9qj8z9VRB3EGsm+MbELTz6wqa5uWhavwF7vyXN0JIN7Nwef2rR3hBodyWDFskhW2TpaLm+6VjjrNF6fL27tag8GbKI2qWeLlp3XFHsjJ8+/mHsn8H3NgsKpN5eSl7i1pzsI9AV1AG7tahdeDhO1UdgtWs6fBYA9U7Ou33c7eQbJKg+yvmn3zMmLePUHP8TCjaJ22pyJaZ1FAZj69lvYcuuqUCVvubWruTjCJupgoyMZz0Qxu2auO37v3aueGfA/s3qg4eeg1rL6absZ27m+JsnQSAi3djUZAzZRhwtarrSd647fe/dq05+DoudZ2c2ZM8lNAk3HgE3U4YKshU/PZD33WNvxvNvdonh/J4+er6qkBpRG5Ew6ay6uYRN1Aa+1cGsqXMc5dW4kBTcKXG/uVmHeWUHp8+P8bAW9+KNocYRN1OW8WmLap86tTmIM1mRRAPZOzeLz00sXfNMzWe0onUlnzcURNlEL+WVp279vrwHdSPlHr1GPNXXuLHNKZFEAnj15sZIxPnn0vOsoXeDdf50axxE2UYv4ZWk7vz+/UGro0GhGt27UkylXKAO8R+FECkulSnUXgArhathTeBxhU88J2uEqal5Z2tbIxSto2jO6wxx/kO5Kfk1JiKxAvTJluO7VZ1na5mPApp4StMNVM4K6X3WoIAk71vGG7dC13EhUfiadMrDv7tsr92f9ZwpiZTkga0rga2+n6HBKnHqKbpS77/BSgYhmFRjRTU1btwdJ2EmKhNpLbf0u9v201xeLNd8n8mMF5Jxmb7budoqOqBiXC9yyZYs6depUuw+Dusi68SPabS3plIH38iYSmjKagwMGBvr76h51uyV2pYxkVeLX3qlZ7fGljKTnlHmmXC7SKgOaSaewcGNRW/wik07hytXryHdBowpqPgHwxsQubJs45rqEkkmncGJ8R+sPrMuIyGml1Ba373GETT3FaxRrJXjpal7PL5gNjbr9CpyMjmTw0NYh1y0z6ZSBezdntC05gaV1aOv4s7m8Z6WqbC7PYE2BWX87QSvrUfQ4wqaeMj2T1TbLqEczRhVu6+cAuO2K2sZICCbv31S5uPz89Bl89dW3qi5uMy1M4OxmXiNsJp1RTxkdyWD/S2e9aySH0IzKTm5Vy7ZNHGOwpvaxTexMz2TxwulszUxUNpfH2HNz+NzXz+DqDfcER2oMp8Sp5zx+1+3aPtJhrWzRVpZOKPkoAAYMnlK6kb1OuNf2Q7OoKsEaKC0zjT03x50IEeFfF/Uc51ry4IBR0yowKHGMPLZNHMO68SPYNnEs0pNUJ5R8VAAWuCbetcJsP7Qzi2wKEhVOiVNPslf4upTLY2XKgIhPO0EX8wtmJWtWsNRYIej+6KC2b1iNZ05e1H7fSApW9PchlzerssSvXl90LXJht6I/WTUqInJj334YttBOJ8wQdQKOsKknOfda5/ImrplFrOgPP1Vunbyc6ZtR9po+fu6y9nvplIGblvXhvbyJTDqFJx/YhAsTu3BifAf23e0//Z83C3h461BkywTUnbZvWA2gvnrhnTBD1AkYsKnnTM9k8eihOdcCJFGPNKMaWXg9zvXFIuYX3GuO26f/dYoKOPLaO1XLBF7bx8JIpwzP5waApGM5wkgK18Jj6JmTF7F2/Aj2v3Q29IVtNpcPvEzUzKWlTse/Cuop1shat9c6agqI5KSjG6EEqXw2OpLBifEd2paIQGlq37rfGxO78OAdtzR0vEBpK9C+u2/HifEd2qAtAApFVblAyKRTmLxvE/eHx9j8gom8WYCRrP5EpYwkHt46hMEB90TMILULmlVlsFswYFNPaUdXqmwujz1Ts1g7fgQ/89iRqt7CFr9Rha5Yhe7Cw21EHnRacnomi2df1a+XB3XT8r7K+v3YzvWuiX3W0ReUqhTfGB3JcAo15ooKWNHfV1ME6MDoRsx84eO4MLHL9SLNb5nIq0EOMWBTj2l38ktRlaYWH/rjb1VuCzKq0FVJ041c3QKe19qj1WnJOpYoJiDstaVHRzK4abl3jqv9xOx2gULxksublRkZq3iQ/aJTl5jmlbDm1yCn1zFLnHpKPRmuzXDi9SuYnslq22ra225a3AqqALUV0HRlIkdHMjj15pWabHNr6hqIdgbCumiwKrcFycC33htnFn96wIBSwHt5E2t8aqRT66wdP4JMOoW1P5HC375+pWqXhH3XhJ0Alc++k+7vkzMuJQzY1FN0vaGvLRYiGVUCqGyr8mMF5EZGFc7A5teU5MDoRmy5dZX2/lGNZIyk4Or1RawdP6I9cetYJ3PdBYp1H69GKdQ62VzeNcjq3hsF1FyMWoL0bu9lDNjUU3QBzm3kWY8EgGV9ggXTP5RYwbHRUYVXYAt7/6hmIMyCquz/DhtU97901vf3GR3JRFoTnlrLfmHorJ1/7+YMjp+7HGkv+m7BgE09xy1gjY5k8OoPfojvvXu1occuIni1L3v3o3aMKtyajLgdS6sFnerOxGR5o5ulUwZWLOur+3XWzTbZl0vsn7dsLo8XTmerutjREiadUey1Yl/m9EwWP7i8EPnj6lhTxuvGj2Dy6HncuzmjbbvZDLpENwB44p6Nvj+fMhK++6ubzS0xLZrd42R5r5xY9rCm7auf/r7an7JfjDIrPBwGbIq1VuzLbPXe7IQsTRlbv9MzJy8iWy6R2oopQL9EN79gfM0seu6vbkQ6YEMVt8z5h7YORX48vSwhgrXjR/DsyYuhlzYSQM1++sEBo+piVJczEabQSr06sUAL+2FTrOm2h1i9d4MmWznZp4MTAZPEWmlwwMDjdzWvLeG68SPaE3AmncL2Davxwums59S4W3ZwFJ7aPdzQ7z28/xXf+unUHs7+8V7bv4DSaLwZs03OqfhmPldYXv2wGbAp1rwCS8pIVv3BCYCHtg7hwGjtlK49QK9MGbh6YxFmIdhnf8BIIG8WWx7Ym3kC8TtRWpndIogsez6IlJHAd3/vkwDc19iDvBZuJ2OKF+uCG4Bvtr8zyEfBayAQ9XOF5RWwOSVOsRamJKcC8OzJizVTW26NPoIGawDo70vijYldePKBTa7FPKxVuqQIoiyBnTcLePTQXFOm7MZ2rq8pLWlnvTqtvp5fXn59G1kKsabKdfXQo6qTTu6CvLr2nAm/j1hUWw3tU+C6i9W4F2hhwKZYC1uSUwF49NBc1Ym90WIgubyJD//uX2LP1CzyZqHSAzudMmAkpaq8ZjHitKeCUk1Zux8dyaCvzh7gzWRVR2s0GWl0JON6gZUyknjygU1tT5jrVkmRwMsjebOAPVOzvhdQURRNcV4ANvO5mimSbV0i8gkAfwAgCeArSqkJx/el/P07ASwA+B+UUn8fxXMHsXb8SM1tFyZ2terpqQFu+6a3b1iNP3/1Ioqav7yCUpWrd8C7FGJQ9q1aSpWqg0k5eazquXUHFQG36mdevKaUp2eybWuw4VVIxTphRlGiUrfnHgAWbiwGfhwKRoC6loz8fsZq6wn4f6Z13wty0d4JBVoaXsMWkSSAfwDwMQBvA/g2gAeVUt+x3edOAP8OpYB9B4A/UErd4ffYUaxhuwVrC4N25wmzPplOGbi+WOyqtUwB8EaAz63udUqnDOy7+3ZMHj3flj3MKSOJezdn8PW/z9a0MjWSgsn7NmF0JKNdYxwcMDDzhY/X/fxc3+5MgwMGdn3k5ppESCMhuGl5H+YXzJoLQXsOiFcujACxKtDitYYdxQj7owC+r5T6QfnJvgbg0wC+Y7vPpwH8mSpdHZwUkbSI3KyUeieC56ceEmZ62ytT2EgI+vsSkfe/bjpBTblPt4xy3euUy5stD1jWsVqJRqfevOL6uhdssxVjO9dj7Pm5mhmM968tautQO7mNuNrRrY0aN79gulYiNIuqUmjHGZDtM1K6Cn5WYZhLuXxluSUOQVsnijXsDIC3bF+/Xb4t7H0AACLyiIicEpFTly9fjuDwqNMFSRYJa/dHb9FOqQcVdL9wlKwJMfuhzy+YGHu+et3ea+o4bxZalniVSadwcPcwLkzswtjO9dh3+Ky2BGwRqDppruivHU+YRRVoHVuXtMbKaL3F+jtwy4UxEoKrNxY7qvd2FAHb7S/feSoMcp/SjUo9rZTaopTasnr1are7UA8JmiwSRiadwvFzlxsaadk7XMWBWVDYd/hs5Wu/5JlW7zu33ke//dH2gPqe5r5Bgq4uaY0J4r3F+jtwK7Jz0/K+mhmcuFdZiyJgvw3gFtvXHwRwqY77ENUIOoXZn5RA+dlWYkkj2zfSKQOT95fWWr1GqgNR7vEKIJc38fnpUrJdXPpJW6OW/S+dDfQ+2l9P3UWH1Z7Ri+791V2nMJB3p9zCDfzs7/4l1o4fwZ6pWSzcWMTB3cM4Mb6jql+7nfXZiWMltCjWsL8N4EMisg5AFsAvA/gVx30OA/jN8vr2HQDea9X69YWJXcwS71DTM9nAU5g3Au6rXtZXCqJhu1LpCio8eMct2ineoE1AovTMyYt45uRFDA4YuHdzBkdee6ftfaPzZiHwbIZ95D+2c71rUQ2v9ozTM1nsO3w29GxMjOtHkYeEwHNpy5krYS0fnXrzivZnFErV8uzFlez7xtu5xh1JpbNyFvhTKG3r+hOl1O+LyG8AgFLqy+VtXX8I4BMobev6NaWUb/o3K531rs9Pn/GsXxy2x7Kdlakcpj6ys1ymPaEpZSTaEpz9WFnXwNLWpk6ISxlbxq7XLg9ndu/0TBZjz83BbOLWOuoO9Z4/WlEJjaVJqaNMz2R9yxW2knMrUau2Bg0OGBjo76tkOdeTMJUUwZMPbKpcbPiVJG0Wa8Y56HtqJAW7f04/e+FkJQCyhjg1U9BtlQ09R5O3dRFFZnomi0cPzcUmWCcAPH5XdXJZK7YGGQmp2apVT7AtKIWx5+YAlKby2tXvOuz7aRZU4GANMFBTa7S7EhpLk1JstLrNZRBuE92tqDd80/K+mrWyehPJzKLCnqlZrHvsyFJ51agONKBMOsVyoNTRjKS0vRIaR9jUVnFvcwmUapMDS8kmuunpRtbVndwyWK3nr3e5wP7SRvkqW9XTrHVkt7aFYzvX47lTF7kPmjrWiv7ai+hW4xo2tU0nlYm0lzls5XFnXEomTs9ksWdqtunPHZZVPtKemZ5OGfjUpptdS5G2yrbbVuHE6/qsYKKgMulU6HavYTHpjGKpXQlQjbD38d13+Gxda6dhR+JufbFHvviK63Ytv20u7ZBMSFObovjJ1JmwZ5dOGfjna4uxnAGi9mhWv3r2w6ZYinvvWTf2/Zj1FtsIe8rPmwXsf+ls1W2P33W7a+vIX7ljyLPPdTu0M1gDpc/Z4ED9ZWStEpYM1mTXjqpoXMOmtvHbqhTlmnCUrD6+rTS/UKpidvzc5cqU3L2bM1VfW1N0W25d1bZuXFEbHDAaLvyyJp3C1evB22mmUwZESnkEa9IpLNxYbHvxGYqnVg86OCVObeO139qaeq532rkX+E3JTc9kXTtedRIjIYELoRhJARQaKpzi1r7TqzUj9bZmFFLhlDjF0uhIBg9tHarZYmRlFY+OZLBiGSeBdKyR/sgXX3Gtczw6ksHkfZs6ejuVX/BNCCrNHCbv24TJ+zc11EXNLTu/3XtvKZ6s81QrMWBTWx0Y3YiDu4eruujYR42duM7davMLJvZMzWJ4f23gHh3J4MT4jpbvu24VpUqVp06M78DoSKbhizy34Dy2c33Xvn5Un4SgKQlnfjh8obazTrRu6i3J2akEQLrOddtc3tQ2KOjW19EtwAb9PZ3Z6ykjie0bVmPbxLGavIA4bqOj9vnx5UZb9mRzhE2xFpc2kQAammoN6o2JXZj5wsfrzmrWZa7G6XWM0tqfqA7Y0zPZwKPh/qRUzezcuzmDF05nK73XrR0B0zPZjl5WoOjperU3GwM2xZrVeD4Obl/zY6Hun04ZlYAwOGCUso+h3w5mD9K7PnJz3ceZzeVr1rWt19E6nmZdfCRaPHd84vUrVb/n5NHzgRPE8mYRYzvX4+DuYQCl1qTOYjjWBVC3XvBQfdqV18ApcYote9nSODj5g/nA900ZyUq5Tie37G0jKZUmI9MzWbxwujaJLIz5BROPPlddUtW+9DA9k8XeQ7OR94FuRza1vTd22M9KkKnuS7l85fG7ZbscNaZdNcUZsKkp7MHWrYyf8/vbN6zG8XOXkc3lkYxpTXGvY0oIsDJlVPbuepUtHB3J4NSbV/DVV99CQSkkpdRK0h4Uoih7Wig3/dj/0lkoVZrGs17rF05nIw/WAJrymH6yuTxue+xlFJQqzV5EfAzWaMq64NFV6Ivr55ai1epZpKrnbt9TU7eyam27rQXqvv/MyaXGEHE96SU9SpsVVWlUG+TIrRG09XsWlMILp7OV1yfqGYX5BRO5vFl5rZ91mfrtdNZrGfVHx0gIFm4sYt34EWybOFaaHXGZHk8ZSTz5wCY8VZ5ep+5VVGh5hTMLC6dQ5HQjEJH2jMCiEr4GeAJP3PORmpG27vWxijDo6oRTa6VTBq7eWKxaurCK1QDQziCtHT/SluOl1hGUEkSb8tgehVM4JU6R063xdXKwBuqpAV7EmGMdGdCPoLO5PLZNHMO1Lhv9diq3Zh9WEpq17xsozZjsf+kst371kHQDtekbwSlxilSYbTW9wCyvI1vTqQDQ5/FXl83lkTeLLTq63pIyknhq9zCe2j0MI8BCpG5pxn5BaiUQckakt7x/bdG1umCzMWBTpMJsq+kl1jr+Q3/8LUQZj73W1YMSac0e81ZJJgQpo/rUJgDu3byU1GcWVd3d1qzXfHomi0cPdXatdqqPWVRtWcdmwKZIxWULVhzlzQJOvH4lssczEoIfT0WwqqWA2cc/3jUJU6qocMMRRBWAPz95EWPPz1VGyEqhrr3VBaUqiZNxTZCk5rOWsFo50mbApkixUUJrGAkAgkimYq31uHaUWmyGItx7cBeBmtFwPdnygwNGZFvvqLM5d8A0GwM2RUq35eXhrUNtOqLuZBZrg0+92rUe16nev7bI4ilUoSsH3AzMEqdI2Yt/OLe8WIVRKF7MosL+l87i1JvRTdeH1YR6J01jFlVLi6QMDhgY6O8rLTd1+NbIuEsZibqSPlu1FMiATZHTdd8a27kej714hlOJMTS/YOLZVy+27fkVSmvyfv2v46KgFFJGsiWf5dyCiZkvfBzAUtEh/g2F8/DWIc8Bw+CAgcfvKpUSnp7JYuy5uVCfxVYtBXJKnFrG2YAiigxnik6zRm5B3+WCUh2zJdDq2+7a3CXi57IHA/vfEAV3/NxljO1cX3nd7KeedGopWANLuwiCShnJltUWZ6UzapvpmSyLTTRRQkoXRZ0yag2rVdPSzul6q9qZLkmvnhGal6d2D7s+l65iHrnzmhGxv6frxo9ol2cy5b7y1mcv49M3oB5elc44wqa26Zas5LhSCrEI1oLoGiZYxU8uTOzC60/cqR1pWrM3jTztsr4ELkzswsHdw1V9s72CNVD6XE/ev6mqlanVOtV5PNbXutmmwQGjMk27beKYb01z0vNaRsibBex/6Sy2TRzzDNbWKL3YpGDthyNsaivWze4N9SbzAKVgVlRK2/XNuaZrHy3Zu8KFPdMZCcHk/ZsiPSF/fvpMVZe2B++4BQdGN3r+HgA8v8eWn81nJAS7P3oLXjid1X7WouI1wmbAprawTqQ80ZCfdMqo6i2ua82qa+VqqWcK2WrIEoUgFxf7XzpbuYC1fm/d34n92Dg93lxW+1y3wUWUnxGAzT+oifz6Xut+xnnistYJrbWhTtrmQ82Vy5t47MUzla/tnx2rXehDW4dwYHSj5+PUs0vBb7tOmM+/W7EVaw+v9TPXbLMQ1u+tO177sbHCYHNZ7XPdWBXPWjE9zoBNdXMGXqvqj0V3InM7cSksrRFFmbDTTAkp/SFT81lrjD/K13bQUgCePXkRW25d5bu2DNROISdFsNxI4OqN2sDotV3H6/Pvdhy6oGrdrgvouuQ6+7GtKSdDUXv4vfdRYdIZ1U13gtk7NVup2axQW77Pq73kvsNnOyJYZ9IpFrBosfkFU5sVrgBttSl7wtbk0fPYvmF1VbJWQSncWCzCSFYnfvlt19F9/h89NOdaOU4X/K3bdX8X1p5vr2NjAlr7taLiGQM21U13glFwr9n8Oy++5pmFCZSmATvB2M71rJseM27NGKxRsP3i8dmTF2sCrVlU6EtIqGxwrwA79lxt0HYLqmI77pWajmnOPd9ux+ascZBJpyqtRDtlb3snSKcMzz3wzV6a4JQ41S3sNNyCWcRCF0zbDRgJ7J2axcqUASMpbK8YI86pSd3yi5u8WQy1Dun1+TeLCvsOn61KlNv/0lntsWRzeRhJqan2Zo2kddUD7bzuw3oHjUsZyUryoy7Jr9kX8RxhU916dRpuwSxCoTwboFDZY0vxYJ+aDDviCTOl6ff5z+VNbJs4hs9Pn8HY83O+2xfNgsJNy/tCjfKDGB3J8DOqYb3OD28d8qy8mEmncO/m0gXguvEjuHp9MfQSShQaGmGLyCoAUwDWArgA4AGl1LzL/f4EwKcAvKuU+peNPCfFh3UiifrqfXDAgFKdMT1uFhV+lF/Ew1uHMPV3b3XE+nsnSqcMXF8sBs7wtgJ12FmgIAHenhmeHjA8jymby+OZk8FrtNvrhgcVJFP98btux96pWe68sBkcMKpe62c175OgdHE29vxcZTYtlzeRkNJj5BbMwDtkGtXolPg4gL9RSk2IyHj56992ud9/BvCHAP6sweejmBkdyVTtHW2UAFVF+Duh0UFBKTx78iJPhiGkUwZuLBawEKCYigDYd/ftAJZ2HiR8ypJaU5NuW7lSRhIJQaCscLc93/biGVEX/VluJFwDMOC+6+KhP/4WTry+1GVNl608OpLhtLiD1VbWep10F3fpAQOfPTRbsyPE+vqNiV3NPtSKhgqniMh5AD+vlHpHRG4G8E2llOucgIisBfAXYUbYLJzSGaZnslVXn426YPsDmJ7JYt/hsx0x2qZwjIQA4t3XWwDXPdZeF3PO6lO6AOj8eSMhMJJSuYhY1pfA9cXqC4pW1AdwPoeRFMBRZjZlJPGvhlZWBWs7t2IeLK5Sy3qdnEVrLG6vvdOFiAN2Mwun/LRS6h0AKAftn2rw8SAijwB4BACGhoYafThqAWcPbL/RjxdnBmazpt2p/fyWD7xqNTv3VOuaMfhNF1vfW5ky8KNrJkxz6ZicwRpoTTEf53O4XdDkzYI2WAPuU/tsb1vrUi6vvfhLGQlcK+erxIXvCFtE/hrAB1y+9TkAf6qUStvuO6+UGtQ8zlpwhN0TvLrdWBIAko4Ma11d3m4cGbDoip4g+DSjV0D2KwVq122fscEBAwP9fTWvi/31Wpkyen7manCg9Bo0UlNB102tXg2NsJVSv+jxwP8oIjfbpsTfbeA4qUsESfT50u5hAPpqaHYsu9hb3LbGBJnWzuby2Ds1iz1Ts8ikU7h6fdG3FKil2z5j719brEzvOte17b/72vEjbTm+uIgiB8Ht89QsjU6JHwbwGQAT5f9/o+Ejoo7nN/WWSacqH/AgH/SB/qRrglAn4+haL5vLY+34EQwOGHj8rlKymVsJUIGq6QBm39es4xac21HaM2WU1siLqlQeNSEKdTY0q2IkapcbdBcqmTb83t2mlRd7je7DngDwMRH5HoCPlb+GiKwRkZetO4nIVwF8C8B6EXlbRH69weelGLOqLqVdKjdZexXd+vvqLHRZsKZqut2v8wsmxp6fcy04kjeDZZi7cRvBj+1cX0qCa6G8WcSyvlJ/7ycf2AR47AMOKiHQBn3dunYv1lKIUisrHjY0wlZK/RDAL7jcfgnAnbavH2zkeajzWFNvQacyvQrnczDa3exd2pzMgop065SuuIX1uWv1jgR7kZcodllYo3W/ZiEWZ8JoesC9hSTpNbtYih37YVPL6RJ8dH1lb3vs5bqzzqm36ZKv/NgvNBvZ9RCENa4O8gxJETx4xy2+xVhSRjJQsp0br3Vttr2t1UnbuohC82sz6BTkBEWdzStr3q3KWZDAYSSlUoQnLHtylq7OgJEQ3LS8r+ERqTXy9VtLtgfd4+cua+9vbW0L26fe/vNuj60bufcyr0YgzcBa4tRyfm0GnQ6MbsTDW4fYdaiLJTVvrpEU7Lv79ppOVA9tHfJfe40otoyOZDB536aqetyCUmKXUqipKW0kBemUAUFphO+1NF4pe+mylmwkBIMDhmtdcd2au5GUSnA+Mb4DB8u7MfZOzVblinjlkLgdS8pIegbrAaP3Qkkraoc7cUqcWi7M/lg3QfZ5U+cbMBL4D/d8RPuZCDJtrVtmqZfbZ9caaetqSk/PZD0L/1hTqkFqgjuPxb7mbmXV++1Dv3dzpqq8qnW7X3U4q0iNUybgDEE38Srq0yivKXEGbGqLMCcn532vXl/s+YIPvSBMsNVdxIUpwhJE2PyLRn+uEbrn1E1t+x2L14W2V2MRETRUmCRumvmeAVzDphgK0t8XqD1J6PoGMxmm+4TZ36rbRx31lpuw+RcWXROSZk6p6o5JN7Xt9zs4M8rtF9q60TdQykHolszzdkyD2/XewgN1lMmj52v24Lr1DT64exhPldfrSM+r5287ZNIpba/mMMFWt+4a9clVd0wKwPD+V7T1BKzaBFH3uq7nWHWfgSCvt7U2/sbELpwY31G1pu6WU5BMSNcE66QI7t0cbKDRLBxhU6zprvrd+gZvmzjWikOKDSuzOkz2bpyyfC9M7KqswzoFDbbO3tTL+hJ4Lx9tf2Lnczhndyy5vImx5+YAuNcTCDqrFBXdqF63ht3IxY3b6HvhxmLsg/XDW4fw1VffCvR3UVAKL5zOYsutq9oWtBmwKdbCTHV2Wz1oP0VVvQf0w7/7l3VX/2oHXZekhCDQSMb58/MLJlJGEgcjbMbg9hxGUrTrsmZRtbS2tBevKewtt66qe9uX1/PZH2NdB9QpPzC6EVtuXRW4i5muxGurcEqcYi3oVOf0TBaJANO98ZoQbtzHvvTNyr/zLu0g42zP1KzrSbKogBdOZz3L1QLuyyV5s4A9ji1MjdAtyXgNyOJ04eicwgZKM1F7y1nrB3cPV01tR6mVJTvrYWW3O5cr0ilDu0wDtPf9ZcCmWAuy9meNgnTTWgNGItz+3Q7yvXevVgJTjGa7G2Yv2anjdeK0yt02GrTrOTnHNVBZfyfZXB4K0b1GOrqL7RX97f/7c1702y9sZh//OGa+8HFt0E57BPNm45Q4xZ7f2p/bKMhucMUy/AfblN+WW1dh/0tnY7++FpRfYOtUfsHSr8NWFNOXuudIpwxcvbHoWv2snVnEXnQzEs2a4nVOya9MGRCJpqVlI4LuodZdALfzwpgjbOp4fid250hidCSDgf7uuVbN5vKVZKduooCaqW17ha6r1xd9H6PR6UvdKHHf3bfXVD9LpwxM3r8pFuvXburdktYIe8W164vFtgdrAIHX63W1HtpZA6J7zlrUs4L0MnaOJPxOUv1JwY0Iuie1ilvWcjfI5vLYOzWLPVOzNaPaICdOa3o6bBUxi1filv37naBVe9Xd+M2CtbKOgq4zoPMzoqtv386tkRxhU8cb27k+UDKZPUjrTlKZdAoXJnbhH37/zpYX9u8kRlLw8NYhzzrZUbHOmbm8GaoFpVWnu9G1W93e407Tqr3qbrwukDPpVEuLHrnlR7h9RnTXwO3cGsmATR1vdCSDhwI0B7EH6SAnL10xCCplSh8/dxlfemA4lq+RAHho61ClCpdu7baXtKN4i8XrAvnE+I6WXxxnc/mqxid+MwB27byQ55Q4dQVrP6VVItE5xeaWFQropzp195m/er2j9jo3kzW9+sQ9G6sSi9pd5z0pgicf2OS7/NGMtdt6p95bpdXFWyx+pVndvt9s9tmWoM/b7tKkbP5BXSnKE6ez0lUcEmfiwq3L2uenz+DZkxfbXttdAAz0J3H1hvvJuBWdvMJ0oet2fn+T9u+vTBkwC8XKe1fPGreRFPQlBPkAF9i6aoHplIEVy/qQzeUr92lmpy6A3bqI6qarxtXtrBNkkLKnboHPOvnGteWiVaLz+LnLkY2GvbpjFZWK5Yi7U0zPZDH2/JxvDoP1ubWCKgB89tCsdj3aLmUkXS+2ALT0QswrYHMNm8hDmLWtbpFOGTi4exgXJnbhyQc2+a5Ru00tW4laT+0ehpEMn5nWzFy2TDqFezdnMPV3b1UlGY09N9dQERGv7litKFTSzUZHMjXb6JwGB5Y+t9YF5GMvngkUrJMi2vX9OOVAcA2byEOcyky2yoplfVXr934XLNZ+abfRo/1xwoy2g47u6zG2cz32HT5bsxXOLCrsO3y27lFTPdsLO1G71unt6+9BjiHMxXZBKe36fjv2r+swYBN5CHISBkqj0jhUcYpCNpevjAKDBllr9AjU7m+1ToRBpzUtBaVqpimjMHn0fFOKYgRNnIrLRWA9gdetP73b+97soB4keS7M6+yV+d3O/etODNhEHoKchK3KV9YJZOSLr3R84N5Tbg4RhtV449FDc9j6M4O48MN8zQn71JtX8MzJi4Eez1qHfPTQXKQj7Watqzt3FSQ0MwRBTvRhA1499w8SeJ2ClDfVPfapN69EmjPgJ+jFtl/mt1+Geysx6YzIh/NkuH3D6poTD7B0oo7vX1T7GAnB7o/eUtOH2e/+x89ddt2m1yyDA0ZNn/V61Zs1HvbndImRgwMGHr/r9srsRpBe1X6Z8+vGj7i+DwLgjXKrV13yndtWy2Zm0Otex3qSDVu5DOCVdMYRNlFIW25dhQOjGytf92omeRhmUeHZVy8GbpxgFlXVSNz+Y4MDBq6bhabsh9/1kZsBeJ+gg568g+z1dxO2SYdurXZ+wcSeqVl87utncGOxWFmz9xp11ttwxT5roHsM51vf7PX8el9/3WPFIe+AI2wiD9MzWYw9N1eVoGQkpKrJg25E0Y2MBNCXjH5dOShBqYdzs177dMrAvrtvd70AS6cMfGrTzTWzBM6RYqOjsSCj2CD3r4d9hO32ewDQ/j0A4ZMLLQNGAgqo2jNtnyFwinuBmkZwWxdRnbyyiS1xSSJqBbMI3Ls5U0nSaXUbBAXg0UNL26+ifu1zeVM7Ys3lTTx78qLnFp8oek7r1rjD3h6WfV3289NnsGdqtnrb2/NzOPXmldo3XYBTb16p/N5u/D4nC2axpsDJ/IKJsedrt9q1uq93nDBgE3kIkk3cjmzRdjp+7nJlj/XykHXEo6g7XlAKj714Bp+fPtPwY7nxugjQjWSzuTy2TRzD/pfONrxnN2yTju0bVgd+bLt0ynDddzw9k3VNDDQLCn/+6sWaLH+zoPDVV9/Szrqs6E/ioa1Ddb33ZkHVvHZx2hfdalzDJmpQO+ogt5M1iqqnqIxVd7zRaey8WQicbe7ktb97cMDAQH9fXcfXyNqwXdi11+PnLoc7UNTubLCmmPdOzSLh0T6yng5WCzcK2HLrKgCoq2St87WL077oVmPAJvIwqKkdbq+45HaC9Tp5L+tLIGUk8V7exHIjEajWcdwM738l9J7lTDpVea3C7McOa6D8mro9urVVzLkOCwDJhODxu273Pb56MtbDzsKESXKqJ1DduzlTCdDpAQPvX1usvB71bKHzugiyljF+bHlfXWvtztcuTvuiW41T4kQeHr/r9prSmkZy6cRucfZM1hViWNGfxPkDn8Ts4x/Hwd3DuNaBwRoIX2DESEjV9je3YDg4YODCxC7P8pNBLGiCtdUfe3Qkg8n7NyGdWnqewQEDT5YTCUdHMljR7z6Wsdp2hmmx2Ow9u2EDVTpl4IXT2coa8PyCWXPxopMyEq7T9Q/ecYvnOnVBqbqL0jhfu3b29W43jrCJPNS7NURXbOH3f2lpO9jk0fO9s2fbdjbXjQhz5ZmMx+9yz9JulMLS++k3gn1PE1wUUNnSp8tQTxkJ3FhUKCiFpAju3dzcLUFjO9dj79Ss9iLFufdZBHW9tgkAT9zzEZx68wq++upbVb+f9ZrUu0yh83C5p7md7m8SKL0n3Zg5bmHAJvJRzx7MIIG+V7aCAaXkoUcPzVXWSL0qgFmvkVeFs5SRwGJB1YwMjaTgpmV92qIgQemmXe2P4XZRZiQEi0VVOe6CUnjhdBZbbl3V1P3Gp968UrM+rCsSsjdgFTsRVPbNW9vdAOCF01nX388K2lG0Vk2K4ME7bqmqd2Dn/Just3Jbp2HAJmoSv0BfT3OLZjXEaAX7Sd7JOaVpvW66EqnXzCIO7h7GvsNnK1Ot1r5dwL0dYpgp0yDlKN0uytwqiLWi4ceB0Y3YcuuqQDNBQZL+dFXItk0c8yzqYh1Ho+Vkw17ohC0206kYsInaJOwJzUhK0xK12sGrT7SVtayzppzA5lf3OpvLIylSte0n6Am8lBBYCgLWCFPX2MSybvyI62O1IoM56EyQ68xAUrCivw/v5U3PYB8kQ9v6uUaXNcIE3F7JHGfAJmqTTMDmBEApWe3GYmcmqOnoLlj8Sr3qRstuNd/tVcmCTpO6Pf/1gK99J2QwN1KyM+jvV29bVaegAbcTXvcoNFSaVERWAZgCsBbABQAPKKXmHfe5BcCfAfgAgCKAp5VSfxDk8VmalLpZkBrk1jakKPYux5l9vdXr98xogovba+m1/Ur3OIA+mcyvMYbuOJrd5KKV6vn9dD8TZPQd5DWv97jiqpmlSccB/I1S6kMA/qb8tdMigEeVUj8LYCuA/0VEPtzg8xJ1vNGRDJ64xz2pBigFnBPjOzA6kvEcaTS6DSoO8mYBz568GOiiZO/ULLZNHKsqRem2huk1FPEqZ6l7re19wnWs99Stglg3qOf30/2MXxJgmLyDbn/dLY2OsM8D+Hml1DsicjOAbyqlPF9hEfkGgD9USv2V3+NzhE29IMiITnefpAiefGBT14/AAe/2jPU2wHAbwXk1FOnUUVscec2KeM2AdLtmjrB/Win1DgCU//9TPgeyFsAIgFcbfF6irhGkEITbfYClutrbN6yOpE53nOnaMwLAylR9swxuo2nda+18TmqM26j44O5hXCgXH+rFYO3HN+lMRP4apfVnp8+FeSIRuQnACwD2KKV+5HG/RwA8AgBDQ0NhnoKoIwVJAvLam5w3Czh+7nKlTneQ8qhxU0+5T2Ap4OrKXw8YCQyuWKZ9LdakU66tGp+4Z6N2S1m3ZR63U1z6THcK34CtlPpF3fdE5B9F5GbblPi7mvsZKAXrZ5VSL/o839MAngZKU+J+x0fUDYKcuEZHMtqiF5dy+ZrHiGOf7hX9SSzcKFQFZwHwr29bhb+/+F7obUBWFnDOpVAKUOqv/J3xHdqkpO0bVlfVDbfaSE7et0mbxd9tmcfUORqdEj8M4DPlf38GwDecdxARAfB/AviuUupLDT4fUU8L0xPZa2q3Xa46gjVQGlmfvfTPWG6EOx0ZyaX65H6viy4p6chr77i2i9z/0tmerlndTNMzWWybOIZ140dqkge9vkeN78OeAHBIRH4dwEUA9wOAiKwB8BWl1J0AtgH4VQBnRGS2/HO/o5R6ucHnJuo5QSpwWfyqhcVJPY0hVvT3LXX/CliZzDmLoXtt5hfMhvYrkzuvEqIAeqK8aCMayhJvNmaJE9VyW3P1OqHFcWo8KvZs4rCvCwCs1VQmA4ALE7uiPtye57UjAnCvrx90L3a38MoSZ6Uzog4TNlHHbfTpZsBIQEEi75LViHTK8Bx9O0dhYUdiusdP15l1Tt7qKSHKJL8l7IdN1OWc67c6ebMYqKBFq2TSKcw+/nE8tXvYcy0+bxawx6WYShD77r4dRsLR7zwhlc5UFC2vXIMw+Rm9igGbqAeMjmRwYnwH3pjYpQ3IVkONE+M72h607evP9gsOL17Vy3RGRzKYvH9TVTLa5P2buGbaJF6JfEzy88c1bKIeE6Tu8vRMFnunZhvua1yvp3YPh6rzbddra56dxivXoJ48hG7DNWwiqghaqKVd2eVJXRUUBFuP55pnvHnlGrCQijcGbKIeFOTEGKb9Z5SscqtA7XaeIG0b0wMGtk0c8x2lcTQXP3xPvHENm4hctbPwilfNbmud3S0ZzUgK3r+2iGwuDwX9uvb0TBZjz89V3W/s+TkW6mgja6nG773rZQzYROTKmV3uNVXdDNbUtlv1K2skljcLlePKpFNY0d8Hs1hba90Z/Pe/dFZb4Yzaw61FKputVOOUOBFp2afO3ZLVmslqzOGsfjX2/BygUAnMBaUq2cS6WuvZXB7bJo5VpljnNbXHdbdT89WzR7vXcIRNRIGMjmRw7+bWrCcKSkH20UNzNRcIZkFpR9Fee3Y5xRpv3IftjwGbiAKZnsniq6++1fTnsbfadLYS9XIpl/ddd7cCu66SGSuctQ/3YftjwCYiX9bUdJgAWq96n8Eq/OJXZOVSLs8KZzGk66jGLPElXMMmIl9uCUFxs33DagBL6+66IitWYAeq96Jv37Aak0fPY+/ULLcUtQn3YXvjCJuIfHVC4s8Lp7NV69N+U6z2cq1jO9fjhdNZbimiWGPAJiJf6YH4r+3atwDptn05p1itLWN7pma5pYhijwGbiHzFuOVAlUu5fFUBDmApce3q9cWq+zrv5yZbfjyiOOAaNhH5es+jJ3WcrEmntOvtubxZVfI06Lr83qlZnHrzCg6Mbgx0DCyvSc3CETYR+WpkL2yrypsKSolnXuvt9mnuoOvyCsCzJy8GGmmzvGYwbtXryB8DNhH5Gtu5HvUUJk2njJqtOs1iBVa/2XsrUIe5CFFAoPVsltf0x4ua+jFgE5Ena4q3nmXsXN7Eo4dKTTas6eGogrbbBUTQY1w3fgRXry/CSFY/itdFSZAROctr+uNFTf0YsIlIK0hilh8r6csaSW3fsDqSafJ68+BU+b9c3gQUMDhgVEb/D20d0gbtICNyltf0F/SihtPmtRiwiUgr6oIpebOA4+cu10yTP7V7GBcmdjV1ytyNWVQY6O/DGxO7cGJ8Bw6MbnQN2kFLZG7fsLrun+0VQS5qOG3ujlniRFTDmgZvZGStYz3mifEdNd8b27keY8/P1bS+bCZ7G08ru3tlyoAIkFswA2V6T89kse/w2dKovYbC3qlZTB49X1fGeLdlnY/tXF/T9c15UeM1bd7Jv3ujGLCJqEor2mjat1fZn3fy6HmYBYWEAMWAMTsp4lrj3EgIVizr0wTRJW5tPHN5EykjiYO7h30DhN/rlTeLAJZGiQACBx239qJhHyMKUV40uJWFdT4ecwHciYpxRYQtW7aoU6dOtfswiHqKrgZ31JIiKCpVqeP9wulsVdCzunZl0ilcvb6oDbwC4ODu4ZqRuZEU7P65W2oe1y5lJPHEPRu1swmZdMp1JsAu7OsV5DH9HjvMYzTK7YLEet2addEQh9+7XUTktFJqi9v3uIZNRFVaNYopKFVZn3z25MWaoGqF3mwuj6s3Fmt+3mIVS3FOo5sFVbNePjhgIJ0yarpBNTKiC/t6hbl/HEaaQbK6o04QY6tNd5wSJ6Iqa9KpyEbYuulqJ797mAWFFf1JLNwoVN3XOonvnZp1/blLuXygDlC63zkhgnXjRzyngcO+XmEyxnWP3cqsc7+LhmZM2weZNu9FHGETURW30U09jGSwYB3Uwo0CDu4edu2X3Oh2Kt3vbJ8F0GUp6352RX+ypud22FFiHEaafq9ts/ZV27upnRjf0fPBGmDAJiKH0ZFM1TRyOmVgRb93AE+nSlPNlsEBA5P3bQq1TcuvkprVx9rtJN5oYHP+zlaHLztdEHL+rLVN7ewXP4HJ+ze5XmAE5fbYzVw7duP32sZh2r5XMOmMiHx5JVZ5JSAFzThPGUncuzmD4+cuI5vLVxLOgjyH/bmimkJdN37EdZpeALwxsauux3TqpO1aXsfaywlizeCVdMY1bCLy5TVa8gqk1u2PHprTTo8nZKmgihUI6glmQdaqg2rG2rH9d0oPGHj/2iLMYnUVOKC127WC8nptg+yrpmgwYBORL10Ay5Snqb2MjmS0SWHA0n5rZ9BqZ+CKOgg5ZxrmF2q3qAUpDBLHUTkTxFqHAZuIfDUawIJmUselmlXUQShoiVevmYy4FFFx0+4LrF7BgE1EvhoNYG4BXycuyUpRBqGgv5PXlDvLdRIDNhEF0kgAs35uj8fUuKUbO1sFmWHwm7FgNjZxWxcRtUSQYN+tyUpuW6OMpLhWXdNh605qaIQtIqsATAFYC+ACgAeUUvOO+ywH8F8ALCs/3/NKqccbeV4i6kwZj5FmpouTlaJYE2c2NjW0D1tE/jcAV5RSEyIyDmBQKfXbjvsIgBVKqfdFxADw/wL4LaXUSb/H5z5sou7SjkYS3SSOWeIUrWbuw/40gJ8v//tPAXwTQFXAVqUrgvfLXxrl/+JbrYWImoZbgILRBWZmY/e2RgP2Tyul3gEApdQ7IvJTbncSkSSA0wD+GwB/pJR6tcHnJaIO1YlBp5Uj2zhv36L28g3YIvLXAD7g8q3PBX0SpVQBwLCIpAF8XUT+pVLqv2qe7xEAjwDA0NBQ0Kcgoi7kFyhbEUhbHUC5fYt0fAO2UuoXdd8TkX8UkZvLo+ubAbzr81g5EfkmgE8AcA3YSqmnATwNlNaw/Y6PiLrT9EwWY8/NVZXvHHtuDqfevOJac7xZgbTVAZTbt0in0W1dhwF8pvzvzwD4hvMOIrK6PLKGiKQA/CKAcw0+LxF1uX2Hz1aCtcUsKjxz8mIl09x5RR9FW0enqAPo9EwW2yaOYd34EWybOFbTspPbt0in0YA9AeBjIvI9AB8rfw0RWSMiL5fvczOA4yLyGoBvA/grpdRfNPi8RNTlcvnaettBRD0SjTKAWtPr2Vxe22c7Dj2wKZ4aSjpTSv0QwC+43H4JwJ3lf78GYKSR5yEiCirqkWiU+5+DTK8zk550WJqUiGJpcMBw7WrlxUgIFm4sYt34kcgCXZQBNOj0eidm0lPzMWATUSw9ftftGHt+DmZhaaU6IUAyIVW3WYln6ZSBqzcWK0E+yiS0qAJoM/psU+9gLXEiiqXRkQwm79uETDpVqbf9pQeGa247uHsYFyZ2YcWyvqpADixNN/slerUK16epEQ2VJm02liYloqDWjR/RllBMGcnYlENleVHy0szSpEREsaCbbk6KxKoQCdenqV4M2ETUFXTZ3M5gbXFLALOPftMDBpQC3subHAlTLHANm4i6wuhIBk/cs7Fqfdv62o0z0cu5R3p+wUQub2r3SxO1GkfYRNQ1dNPNQfZRu+2RtmM9b2o3Bmwi6mpB91EHqZDGet7NxYQ8bwzYRNT1giR66ZLWnPeh5mBbUX9cwyYigvseaTsp34eaw6tsK5UwYBMRYSlpLZ0yar4nAB7aOsSRXhOxrag/TokTEZVZU+dcS209lm31x4BNROTA4iatF2VXtG7FgE1ERG3HtqL+GLCJiCgWOLPhjUlnREREHYABm4iIqAMwYBMREXUABmwiIqIOwIBNRETUARiwiYiIOgADNhERUQdgwCYiIuoADNhEREQdgAGbiIioAzBgExERdQAGbCIiog7AgE1ERNQBGLCJiIg6AAM2ERFRB2DAJiIi6gAM2ERERB2gr90HQERE8TI9k8Xk0fO4lMtjTTqFsZ3rMTqSCfXzj734GvJmEQCQEOBX7hjCgdGN2vs/emgWBbV0W1KAJx8YDvW83Y4Bm4iIKkrB9gzyZgEAkM3l8diLZwAgUPCcnsnis1OzKNpuKyrgmZMXAaAmaE/PZLFnarbmcQoKldsZtEs4JU5ERBWTR89XgrUlbxYwefR84J8var731Vffcr2/3+NRCQM2ERFVXMrlQ90e5n4FpWpu83vcoM/bCxoK2CKySkT+SkS+V/7/oMd9kyIyIyJ/0chzEhFR86xJp0LdHuZ+SZHQjxv0eXtBoyPscQB/o5T6EIC/KX+t81sAvtvg8xERURON7VyPlJGsui1lJDG2c33gn9cFlgfvuMX1/n6PRyWNBuxPA/jT8r//FMCo251E5IMAdgH4SoPPR0RETTQ6ksET92xEJp2CAMikU3jino2BE79GRzL40u5hpIyl8JIQ4OGt7lnioyMZPLV7GEnH4DspwFO7mSVuJ8plTSHwD4vklFJp29fzSqmaaXEReR7AEwB+DMC/V0p9yuMxHwHwCAAMDQ1tfvPNN+s+PiIiok4iIqeVUlvcvue7rUtE/hrAB1y+9bmAT/4pAO8qpU6LyM/73V8p9TSApwFgy5Yt9V9NEBERdRHfgK2U+kXd90TkH0XkZqXUOyJyM4B3Xe62DcDdInIngOUAflxEnlFKPVz3URMREfWYRtewDwP4TPnfnwHwDecdlFKPKaU+qJRaC+CXARxjsCYiIgqn0YA9AeBjIvI9AB8rfw0RWSMiLzd6cERERFTSUGlSpdQPAfyCy+2XANzpcvs3AXyzkeckIiLqRax0RkRE1AEYsImIiDoAAzYREVEHYMAmIiLqAAzYREREHaCh0qTNJiKXAURZm/QnAfxThI9H9eN7EQ98H+KD70V8tPO9uFUptdrtG7EO2FETkVO6Gq3UWnwv4oHvQ3zwvYiPuL4XnBInIiLqAAzYREREHaDXAvbT7T4AquB7EQ98H+KD70V8xPK96Kk1bCIiok7VayNsIiKijtR1AVtEPiEi50Xk+yIy7vJ9EZH/o/z910TkX7XjOHtBgPfiofJ78JqI/K2IbGrHcfYCv/fCdr+fE5GCiNzXyuPrJUHeCxH5eRGZFZGzIvJ/t/oYe0GA89NKEXlJRObK78OvteM4qyiluuY/AEkArwP4GQD9AOYAfNhxnzsB/CUAAbAVwKvtPu5u/C/ge/GvAQyW//1Jvhftey9s9zsG4GUA97X7uLvxv4B/F2kA3wEwVP76p9p93N32X8D34XcA/Mfyv1cDuAKgv53H3W0j7I8C+L5S6gdKqRsAvgbg0477fBrAn6mSkwDSInJzqw+0B/i+F0qpv1VKzZe/PAnggy0+xl4R5O8CAP4dgBcAvNvKg+sxQd6LXwHwolLqIgAopfh+RC/I+6AA/JiICICbUArYi609zGrdFrAzAN6yff12+baw96HGhX2dfx2lmQ+Knu97ISIZAL8E4MstPK5eFOTv4l8AGBSRb4rIaRH5ty07ut4R5H34QwA/C+ASgDMAfkspVWzN4bnra+eTN4G43OZMgw9yH2pc4NdZRLajFLD/TVOPqHcFeS+eAvDbSqlCaUBBTRLkvegDsBnALwBIAfiWiJxUSv1Dsw+uhwR5H3YCmAWwA8BtAP5KRP4fpdSPmnxsWt0WsN8GcIvt6w+idHUU9j7UuECvs4h8BMBXAHxSKfXDFh1brwnyXmwB8LVysP5JAHeKyKJSarolR9g7gp6j/kkpdRXAVRH5LwA2AWDAjk6Q9+HXAEyo0iL290XkDQAbAPxdaw6xVrdNiX8bwIdEZJ2I9AP4ZQCHHfc5DODflrPFtwJ4Tyn1TqsPtAf4vhciMgTgRQC/ytFDU/m+F0qpdUqptUqptQCeB/A/M1g3RZBz1DcA/Hci0iciAwDuAPDdFh9ntwvyPlxEaZYDIvLTANYD+EFLj9Khq0bYSqlFEflNAEdRygL8E6XUWRH5jfL3v4xSBuydAL4PYAGlqyiKWMD34gsAfgLAfyqP7BZVDAvud7qA7wW1QJD3Qin1XRH5vwC8BqAI4CtKqf/avqPuPgH/Jn4PwH8WkTMoTaH/tlKqrd3UWOmMiIioA3TblDgREVFXYsAmIiLqAAzYREREHYABm4iIqAMwYBMREXUABmwiIqIOwIBNRETUARiwiYiIOsD/D4UqoQTb+50vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a plot of enacted's relative deviation from HD-expected GOP lean, vs that HD-expected lean\")\n",
    "plt.scatter(HDvGOP,relDevnGOP)\n",
    "plt.show()\n",
    "print(\"... and now absolute devn\")\n",
    "plt.scatter(HDvGOP,devn_vGOP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "30165c2f-8e02-4ab8-a934-52aa186d600c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a plot of enacted's relative deviation from HD-expected pct Hisp, vs that HD-expected pct Hisp\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... and now absolute deviation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... and now absolute deviation in Hisp, but with HDvGOP as x-axis\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a plot of enacted's relative deviation from HD-expected pct Hisp, vs that HD-expected pct Hisp\")\n",
    "plt.scatter(HDpctHisp,relDevnPctHisp)\n",
    "plt.show()\n",
    "print(\"... and now absolute deviation\")\n",
    "plt.scatter(HDpctHisp,devnPctHisp)\n",
    "plt.show()\n",
    "print(\"... and now absolute deviation in Hisp, but with HDvGOP as x-axis\")\n",
    "plt.scatter(HDvGOP,devnPctHisp)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "6909a9c8-6c12-43a4-a157-098d686aa2fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a plot of enacted's relative deviation from HD-expected pct Black, vs that HD-expected pct Black\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... and now absolute deviation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... and now absolute deviation in Black, but with HDvGOP as x-axis\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a plot of enacted's relative deviation from HD-expected pct Black, vs that HD-expected pct Black\")\n",
    "plt.scatter(HDpctBlack,relDevnPctBlack)\n",
    "plt.show()\n",
    "print(\"... and now absolute deviation\")\n",
    "plt.scatter(HDpctBlack,devnPctBlack)\n",
    "plt.show()\n",
    "print(\"... and now absolute deviation in Black, but with HDvGOP as x-axis\")\n",
    "plt.scatter(HDvGOP,devnPctBlack)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "629f3bb1-3ebe-4b8d-a7bb-81301c8433dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is a weighted histogram of the distribution of these relative devns from tract-expected R-D leans\n",
      "starting with R-D lean\n",
      "here is a normal distribution\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and now pct GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and now CVAP pct Hispanic\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and CVAP pct Black\n"
     ]
    }
   ],
   "source": [
    "print(\"Here is a weighted histogram of the distribution of these relative devns from tract-expected R-D leans\")\n",
    "print(\"starting with R-D lean\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "print(\"here is a normal distribution\")\n",
    "ax.hist(normalDevn, bins=n_bins)  #no need for weighting function for our simulated normal distro\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "n_bins = 50\n",
    "dBin = 0.25\n",
    "maxBin = 8.\n",
    "n_bins = []\n",
    "totBins = int(2*maxBin/dBin)\n",
    "for bb in range(totBins):\n",
    "    BIN = -1.*maxBin + bb * dBin\n",
    "    n_bins.append(BIN)\n",
    "#n_bins = [-3,-2.5,-2,-1.5,-1,-0.5,0,0.5,1,1.5,2,2.5]\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "print(\"and now pct GOP\")\n",
    "ax.hist(relDevnGOP, bins=n_bins, weights=tractPop)\n",
    "plt.show()\n",
    "print(\"and now CVAP pct Hispanic\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(relDevnPctHisp, bins=n_bins, weights=tractPop)\n",
    "plt.show()\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(relDevnPctBlack, bins=n_bins, weights=tractPop)\n",
    "plt.show()\n",
    "print(\"and CVAP pct Black\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "77d471cf-776d-4644-af56-b1827b87b8f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for d in range(nDistricts):\n",
    "    plotPoly(enactedGeom[d])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "5f543744-d3ad-42d5-b32e-f52a04c610a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is where R-D lean is 2.0 redder or bluer than HD expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "minRelDevn = 2.\n",
    "print(\"Here is where R-D lean is\",minRelDevn,\"redder or bluer than HD expectation\")\n",
    "for d in range(nDistricts):\n",
    "    hi = \"hi\"\n",
    "    #plotPoly(enactedGeom[d])\n",
    "plotPoly(enactedMAP)\n",
    "for t in range(nTracts):\n",
    "    if relDevnGOP[t] > minRelDevn and tractUse[t] > 0.1:\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker=\".\",color=\"red\")\n",
    "    if relDevnGOP[t] < -1. * minRelDevn and tractUse[t] > 0.1:\n",
    "        hi = \"hi\"\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker=\".\",color=\"blue\")\n",
    "plt.rcParams[\"figure.figsize\"] = (8,6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "24c47450-1ed9-4b87-acdd-bc006cb4e4fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is where pct Hisp is 2.0 lower (gray) or higher (gold) than HD expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "minRelDevn = 2.\n",
    "print(\"Here is where pct Hisp is\",minRelDevn,\"X lower (gray) or higher (gold) than HD expectation\")\n",
    "for d in range(nDistricts):\n",
    "    plotPoly(enactedGeom[d])\n",
    "for t in range(nTracts):\n",
    "    if relDevnPctHisp[t] > minRelDevn and tractUse[t] > 0.1:\n",
    "        hi = \"hi\"\n",
    "        #plt.scatter(tractCPx[t],tractCPy[t],marker=\".\",color=\"gold\")\n",
    "    if relDevnPctHisp[t] < -1. * minRelDevn and tractUse[t] > 0.1:        \n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker=\".\",color=\"gray\")\n",
    "plt.rcParams[\"figure.figsize\"] = (8,6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "137352e9-338a-470a-94f6-4a2675bddf28",
   "metadata": {},
   "outputs": [],
   "source": [
    "###  *** BELOW THIS ARE LEFTOVER BLOCKS FROM EARLIER CODES.  STOP HERE FOR TX 8/5/22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "0c91574b-4618-4ae4-8845-0093db0a3103",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is where pct white-collar is 1.5 less (red) or more (blue) than HD expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "minRelDevn = 1.5\n",
    "print(\"Here is where pct white-collar is\",minRelDevn,\"X less (red) or more (blue) than HD expectation\")\n",
    "for d in range(nDistricts):\n",
    "    plotPoly(enactedGeom[d])\n",
    "for t in range(nTracts):\n",
    "    if relDevnPctProf[t] > minRelDevn:\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker=\".\",color=\"blue\")\n",
    "    if relDevnPctProf[t] < -1. * minRelDevn:        \n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker=\".\",color=\"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b5313f9a-e45d-416a-b88a-6233459f67d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is where pct w Bach degree is 1.5 less (red) or more (blue) than HD expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "minRelDevn = 1.5\n",
    "print(\"Here is where pct w Bach degree is\",minRelDevn,\"less (red) or more (blue) than HD expectation\")\n",
    "for d in range(nDistricts):\n",
    "    plotPoly(enactedGeom[d])\n",
    "for t in range(nTracts):\n",
    "    if relDevnPctBach[t] > minRelDevn:\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker=\".\",color=\"blue\")\n",
    "    if relDevnPctBach[t] < -1. * minRelDevn:        \n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker=\".\",color=\"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NJ 26May\n"
     ]
    }
   ],
   "source": [
    "date = \"26May\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "districtCPx = [0.]*nDistricts\n",
    "districtCPy = [0.]*nDistricts\n",
    "districtNo = [0.]*nDistricts\n",
    "for d in range(nDistricts):\n",
    "    districtCPx[d]=enactedGeom[d].centroid.x\n",
    "    districtCPy[d]=enactedGeom[d].centroid.y\n",
    "    districtNo[d]=d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize the red-blue of the enacted map\n",
    "for d in range(nDistricts):\n",
    "    redd = min(max( 0, ( EDvGOP[d] - 0.5) * 3.0 ),1)\n",
    "    bluu = min(max( 0, (0.5 - EDvGOP[d]) * 3.0 ),1)\n",
    "    plt.text(districtCPx[d],districtCPy[d],round(d+1+EDvGOP[d],3),color=(redd, 0,bluu ) )\n",
    "    plotPoly(enactedGeom[d])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e2e3e9eb-e786-4f98-b1aa-5c57af7150a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SKIP THIS - analyzing Ohio legislative\n",
    "nDistricts = 99\n",
    "OHfile = pd.read_csv(\"Johnson2.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "39f93566-3185-455f-8512-602347708e79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# USUALLY SKIP THIS -- USED FOR IMPORTING RED/BLUE LEAN FILE INSTEAD OF CALCULATING\n",
    "demVote = OHfile[\"pctDem\"]\n",
    "gopVote = OHfile[\"pctRep\"]\n",
    "nDistricts = len(demVote)\n",
    "EDvGOP = [0.]*nDistricts\n",
    "for d in range(nDistricts):\n",
    "    EDvGOP = gopVote / (demVote + gopVote)\n",
    "EDvGOP = EDvGOP.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of enacted district pct GOP by district for NJ\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE ENACTED red-blue lean in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of enacted district pct GOP by district for\",STATE) \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(EDvGOP, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of enacted VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE ENACTED home district pct Hispanic in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of enacted VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(EDvHisp, n_bins) #bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts is 6.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy enacted districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for d in range(nDistricts):\n",
    "    if (EDvHisp[d] > minHisp1) :\n",
    "        sumHispDistricts += 1\n",
    "        if (EDvHisp[d] < minHisp2):\n",
    "            plt.scatter(districtCPx[d],districtCPy[d],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(districtCPx[d],districtCPy[d],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts is\",sumHispDistricts)\n",
    "for d in range(nDistricts):\n",
    "    plotPoly(enactedGeom[d])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of enacted VAP pct Black by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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SQ0l+Ncn/VtUHkryxu5/fqrlHNOelt25J8mtJ/nHy++pxV5Cebs79/OOsXmvzZ0leSvIna06aYI0595M5Oc0cgCH5iA+AIQkUAEMSKACGJFAADEmgABiSQAEwJIECYEj/B8vU+YVlmSBkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE ENACTED home district pct Black in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of enacted VAP pct Black by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(EDvBlack, n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 3.0 OH seats out of 15\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #UPDATED FOR ENACTED\n",
    "isOppor = [0]*nDistricts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nDistricts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-EDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * EDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if EDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(districtCPx[t],districtCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += 1./float(nDistricts)\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(districtCPx[t],districtCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = enactedMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the FL map with Hisp+Black greater than  0.4 or even 0.5\n",
      "                        1 light opportunity districts and  8 OPPOR districts\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy enacted districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "counter=0\n",
    "counter2=0\n",
    "for t in range(nDistricts):\n",
    "    if (EDvBlack[t] + EDvHisp[t]) > minMinor:\n",
    "        if (EDvBlack[t] + EDvHisp[t]) > minMinor2 :\n",
    "            plt.text(districtCPx[t],districtCPy[t],t+1,color='blue' )\n",
    "            plotPoly(enactedGeom[t])\n",
    "            counter +=1\n",
    "            counter2 +=1\n",
    "        else :            \n",
    "            plt.scatter(districtCPx[t],districtCPy[t],marker='.',color='gray' )\n",
    "            counter2 += 1\n",
    "print(\"                       \",counter2-counter,\"light opportunity districts and \",counter,\"OPPOR districts\")\n",
    "plotPoly(enactedMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of ED area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(\n",
    "    EDarea,EDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"district area\", ylabel=\"enacted district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 100  #normally 1000 for HD code, but we have <100 districts ...\n",
    "dV = 1./nBins\n",
    "binDistricts = [0]*nBins\n",
    "\n",
    "for d in range(nDistricts):\n",
    "    binNo = int(EDvGOP[d]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binDistricts[binNo] += 1  \n",
    "\n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binVote, binDistricts,width = 0.01)\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"number of districts\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "#we will differ here from the HD code; do a simple stair-step instead\n",
    "stepStart = [0.]*(nDistricts+1)  #this is the minimum vote for the GOP to win x number of seats, not x-1\n",
    "stepStop = [0.]*(nDistricts+1)   #this is the max vote for the GOP to win x, not x+1 seats\n",
    "sortedEDvGOP = [0.]*nDistricts\n",
    "sortedEDvGOP = EDvGOP\n",
    "sortedEDvGOP.sort(reverse=True)  #ordering the districts from most GOP to least to enable stair-step plot\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for d in range(nDistricts+1):\n",
    "    if d == 0:\n",
    "        stepStart[d] = 0.\n",
    "    else:\n",
    "        stepStart[d] = min( 1,max(0,stateGOP + 0.5 - sortedEDvGOP[d-1]) )\n",
    "    if d == nDistricts:\n",
    "        stepStop[d] = 1.\n",
    "    else:\n",
    "        stepStop[d] = min( 1,max(0,stateGOP + 0.5 - sortedEDvGOP[d]) )\n",
    "    xValues = [stepStart[d],stepStop[d]]\n",
    "    yValues = [d,d]\n",
    "    plt.plot(xValues,yValues)\n",
    "\n",
    "ax.set(xlabel=\"statewide vote, \"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "districtRANGE = [0.01*nDistricts, 0.99*nDistricts]\n",
    "districtFifty50 = [0.5*nDistricts, 0.5*nDistricts]\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "#plt.plot(RANGE,districtFifty50)\n",
    "#plt.plot(fifty50,districtRANGE)\n",
    "plt.text(stateGOP,0.5*nDistricts,\"+\",ha='center',va='center',fontsize=20)\n",
    "plt.text(stateGOP +0.02,0.05*nDistricts,\"statewide vote =\"+str(round(stateGOP,3)),color='red')\n",
    "plt.plot(expected,districtRANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#DO NOT COMPUTE responsiveness in absence of fractional seat uncertainty.  See HD code for an idea here\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.  About 20sec to run with 10K bins\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000  #could easily push to 10000, but little gain in accuracy\n",
    "dV = 1./float(nBins)\n",
    "nSigma = 8.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "binWeight = [0.]*nBins\n",
    "binDistricts = [0]*nBins  #how many districts have an expected vote in this bin\n",
    "\n",
    "for d in range(nDistricts):\n",
    "    binNo = int(EDvGOP[d]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binDistricts[binNo] += 1  #one more bin goes in this district\n",
    "    binWeight[binNo] = binDistricts[binNo]/float(nDistricts) #update fraction of districts in this bin\n",
    "\n",
    "binVote = [0.]*nBins  \n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV  #this will store the statewide vote for this bin\n",
    "\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)  #left tail weight\n",
    "highSliverWt = lowSliverWt                #right tail weight = left tail weight\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )  #debug check\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    leftBinNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if leftBinNo < 0:\n",
    "        leftBinNo = 0\n",
    "    smearedBinWeight[leftBinNo] += binWeight[b]*lowSliverWt\n",
    "    rightBinNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if rightBinNo >= nBins:\n",
    "        rightBinNo = nBins -1 \n",
    "    smearedBinWeight[rightBinNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "#plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared \"+str(seatVar))\n",
    "RANGE = [0.0, 0.5*np.max(smearedBinWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0iczMTABf19ADDzzgO8vO108/gRsAevbsyXvvvUdWVhY7d+5k/vz5dOrUKcfq27dvp1KlSlx33XUMHz6cFStWeK5/fr+Pffv2UaNGDSpVqsS6detYtGiR521GwkXjUgsVBLo3qcmWp/tZEChFSmWLoLh5TUP9008/0aFDB/bv309CQgLjx4/nu+++o2rVqrz44otce+21HD9+nMaNG+dIUf3uu+/m2y0Ezhfk999/T9euzj9m5cqVmTJlCpMmTaJHjx706NGD5ORk31gAOFNUXn/99WzcuJGhQ4fm6BYC58vrxhtv5ORJ5/ahp556Cih4Ouv+/fszaNAgPvnkk5CDu9n++te/8oc//IFx48bx+9//3rf8lltu4YcffqBt27aULVuWP/7xj9x5552sWbMmdOD16+O/4oorWLhwIe3atUNEePbZZ31jJ9nWrFnDiBEjSEhIoGzZsrz88sue6g75/z769u3LpEmTaNu2LS1atKBLly6etxluhbkvwO4HKL0sDXUcmjx5MsuWLWPixInRrkpY9OnTh9mzZwdfyW1t0aLwExCFsmd7OgA1zwjcciuscP99F3TaSBsILh0sDbUp1UIGAePT9tFZ7D+W5Xl9awXEBwsEcSglJYWUlJRoV8MUs4IEgQSBcYOtFRAvLBCY+FDAq41Km4IEAWsFxB8LBCY+NG8e7RpETcuHZnI0y9tYoF0SGp/s8lFjSrGCBIHrujSwIBCnLBCY+LB9u/OII20fnVWgIGB3B8cvCwRhUpQ01OvXr/elZU5OTqZq1aqMHz8ecO6ybdu2LcnJyfTu3ZvtMfZlVmLSUB844DxKmIYNG7Jr166wb9frmEB2qmgLAvHNxgjCoKhpqFu0aOFL25CVlUW9evW44oorABgxYoQv59CECRMYM2ZMjiyXJV08pqGO9jF0fmKupyBgcwebbLH9H1dC+KehBnxpqHMHgrp161K3bl1fRstAvvjiC5o0acLvfvc7AKpWreorO3ToUL65dsaOHcv777/PsWPHuOKKKxg9ejRTp07lpZdeYu7cufz000+cd955zJ8/n1mzZjF16lSOHTvG5s2bGTp0aI5UyeAEpJtvvplly5YhItx0003cc889/Pjjj9xxxx3s3LmTSpUq8eqrr9KyZUs+/fRTHn/8cY4fP06tWrV4++23OXLkCJMmTSIxMZEpU6bke2dxSkoKNWvWJC0tjbPPPpshQ4Zw9913c+TIESpWrMibb75JixYtyMrKYuTIkcyePRsR4Y9//CN33XVXvp/lBx98wOjRo0lMTKRauXLMnzKFyZMnM23aNLKysli7di333Xcfx48f51//+hfly5dn5syZ1KxZs0DHeeqppzJq1Cg2b9zA1m3bOL1eEi+88ALDhg1j61Zncvfx48fTvXt3du/ezTXXXMPOnTvp1KmTp7xH+fr8fvhpTY5Fq9L38sKJLCgX/K0JQKeGteDzVLg4cAvWxI/SGQjm9cq7rMFgaH47ZB6G1AB5qBunOI+ju+DrXHmoL0wNuruipqH2FyidxEMPPcRbb71FtWrV+Oqrr/K8x9JQBzZmzBhmz55NvXr12Lt0qW/52rVrSUtL4+jRozRt2pRnnnmGtLQ07rnnHt566y3uvvvuAh8nwKrVa5gx9SPqNWnG0KFDueeeezj33HPZunUrffr04fvvv2f06NGce+65PPLII8yYMSPHnARFtXzrHk54GBNIADo1qhW2/ZrYVzoDQTErahrqbMePH2f69Om+vD7ZnnjiCZ544gmeeuopJk6cmGciGUtDHVj37t1JSUlh8ODBXJmcDO72zz//fF/q6GrVqtG/f3/ASSu9evXqQh9n394XUbFiRV9dv/vuO1/Z/v37OXDgAPPnz+fjjz8GoF+/ftSoUSPoMQTldybvdUzAuoNMIKUzEAQ7gy9TKXh5hdohWwC5FTUNdbbPP/+cs88+m1NPPTVg+dChQ+nXr1+eQGBpqAObNGkSixcvZsaMGSSPGeOrt3/qaP9U0gkJCWRmZnLy5MlCHWelShV9z0+ePMnChQt9gcFfYU4SgvE6JgBYEDAB2VVDYVDUNNTZAk0+s2HDBt/z6dOn07JlyzzvszTUgdNQ//jjj3Tu3JkxY8ZQu3btHME6mMIcZ269e/fOkdQvO6j07NmTt99+G3AC/6+//uqpTvm5aFyq5wRy44ckF2lfpvSKaCAQkb4isl5ENopInusERaSaiHwqIqtE5FsRuTGS9YkU/zTUrVq1YvDgwTnSUGdf5fPTTz+RlJTEuHHjePzxx0lKSmL//v0AHD58mLlz53LllVfm2Pb9999P69atadu2LXPmzOGFF17Is//evXszdOhQunbtSps2bRg0aBAHDhzgySef9KWhHjduHK+99hrff/898Fsa6uTkZAYOHBgwDXWvXr1ITk4mJSUlRxrq119/nXbt2nHWWWf55mbOTkPdo0cPateu7dtO//79mTp1atA5i3P761//ygMPPED37t3JyvrtTPeWW26hQYMGtG3blnbt2vHOO+8ATsro3Gmkwbniqk2bNrRu3ZqeZ59Nu1re+8ULepy5TZgwgWXLltG2bVvOPPNM39/Ao48+yvz58zn77LOZM2cODRo08Fyn3AqSSvq6Lg0sb5DJV8TSUItIIvADcBGQDiwFrlHV7/zWeRCopqojRaQOsB44TVXzPcWxNNRFZ2moI6M401AXZGYxu1nMQPTSUHcCNqrqJrcS7wKXAd/5raNAFXE6TSsDe4DMCNbJlELxlob64WlrPAeB8TaPgPEgkoGgHuDfKZsOdM61zkRgOrAdqAIMUdWTuTckIrcCtwJFakobh6Whjl0PT1vDlEVbPa1rQcB4FckxgkCXRuTuh+oDrATOAJKBiSJSNdc6qOorqtpBVTvUqVMn4M5ibaY1Y7zw/7uelpbhOQjYmIApiEgGgnSgvt/rJJwzf383Ah+rYyOwGch7WUwIFSpUYPfu3RYMTP7KlnUeMURV2b17NxUqVADg3vdXenqfjQmYgopk19BSoJmINAIygKuBobnW2QpcACwQkVOBFsCmgu4oKSmJ9PR0du7cWcQqm1LPvWoqEg7tdS4F/Xlf+JLbVahQgaSkJDo/MZeTHs5zujepaUHAFFjEAoGqZorIncBsIBF4Q1W/FZFhbvkk4DFgsoiswelKGqmqBU7FWLZs2aB3shpTHN4b7VwhPeTR8ObuufbVhZ7uFWhW9xSbT8AUSkTvLFbVmcDMXMsm+T3fDvSOZB2MAeDuu52fbnrvWOH1CiGbXtIURelMMWFMbgHSRZR0XgeHLQiYorIUE8aUUMM/WOVpPQsCpqgsEBhTAl376kIyPYwOW/4gEw7WNWRMFI3+9Fu+274/x7JdB4/x487QOYTqVinPqvS9dr+AKTILBCY+NG8e7Rp45iUIVK1Qhka1Twm5njFeWCAw8SGMM4GF06P9z8rx+tpXF4Z8TwKwelSfCNXIxCMbIzCmhJiWluHpUtFxNi5gwixki0BEygMDgYb+66vqmMhVy5gwu/VW52cJbRkAjPxodch1LIeQiQQvXUOfAPuA5cCxyFbHmAj54Ydo1yCoaWkZHMvMk3g3B0sfYSLFSyBIUlWb6NSYCAp1z0ACWPoIEzFexgi+ERE7DTEmQrzcM2DjAiaSvLQIzgVSRGQzTteQAKqqbSNaM2PigJcB4rIJ2LiAiSgvgeDiiNfCmEhLTo52DQIa/em3IdcZe1Vy5Cti4lrIQKCq/xORdkAPd9ECVfWWBMWYkqKEZh399fCJoOXdm9S01oCJuJBjBCLyF+BtoK77mCIid0W6YsaUdtPSMoKW2wCxKS5euoZuBjqr6iEAEXkGWAi8GMmKGRNW113n/JwyJbr18BPqvgEbIDbFxUsgECDL73UWgSemN6bkSk+Pdg1y8HLfgHUJmeLiJRC8CSwWkanu68uB1yNWI2PiQKjWwHVdGhRTTYzxNlg8TkRScS4jFeBGVU2LdMWMKa28tAbsDmJTnLzkGhoDLABezx4nMMZ487cN6aw9eCTHsqWbfyWrY23f64QDJyi7bp/vtbUGTHHz0jW0BbgGmCAiB3CCwnxV/SSSFTMmrLqWjKtvdh04TpYGv4vYWgOmuHnpGnoDeENETgMGA8OBW4EqEa6bMeHz1FNR2e1jzZJyvG4/Zg7lg9w7YK0BEw1euoZeA84EfsZpDQwCVkS4XsaUSqFuILPWgIkGL0nnagGJwF5gD7BLVTMjWSljwm7gQOdRglWvWDbaVTBxykvX0BUAItIK6AN8JSKJqpoU/J3GlCC7d0e7BiHvJB414Kyg5cZEipeuoUtx8gz1BGoAX+J0ERljCuChqWuCltsNZCZavGYfnQ+8oKrbI1wfY0qlaWkZHDqelW+5dQuZaPLSNXRHcVTEmNIsVLpp6xYy0eSlRWBM7LvggqjuPtTVQtYtZKLJAoGJD3/7W9R2HWqQ2LqFTLR5uXzUR0RqiIjnKSpFpK+IrBeRjSJyfz7r9BKRlSLyrYj8pyD1MSYWWLeQKem8XDWUCgxw110J7BSR/6jqvSHelwi8BFwEpANLRWS6qn7nt0514B9AX1XdKiJ1C3kcxgR3sTvj6uefF/uug3ULVSybYN1CJuq8tAiqqep+4ErgTVU9B7jQw/s6ARtVdZOqHgfeBS7Ltc5Q4GNV3Qqgqr94r7oxBXDkiPMoYZ660nMD25iI8RIIyojI6Th5hj4rwLbrAdv8Xqe7y/w1B2qISKqILBeRGwJtSERuFZFlIrJs586dBaiCMdEVanzAWgOmJPASCEYDs3HO7peKSGNgg4f3BZrFLHfaxTLAOUA/nLuW/yYizfO8SfUVVe2gqh3q1KnjYdfGlAyhxgeMKQm8XDW0Q1V97VdV3SQi4zy8Lx2o7/c6Cch9Q1o6Tu6iQ8AhEZkPtAN+8LB9Y0q8YOMDdrWQKSm8tAgCTVLvZeL6pUAzEWkkIuWAq4Hpudb5BOghImVEpBLQGfjew7aNKZhLL3Uexejz/45kRIcJXN3io4DldrWQKSnybRGISFegG1BHRPyvEKqKk400KFXNFJE7cbqVEoE3VPVbERnmlk9S1e9FZBawGjgJvKaqawt/OMbkY/jwYt/lok27qVsx/3IbHzAlRbCuoXJAZXcd/0lo9uPMSRCSqs4EZuZaNinX67HAWC/bMyaW/PPbK/Its24hU5LkGwhU9T/Af0Rksqr+rxjrZEz49erl/ExNLZbdWcppE0u8DBYfFpGxwFlAheyFqvr7iNXKmBgX6moh6xYyJYmXweK3gXVAI5xLSbfgDAQbY/JhVwuZWOJpqkpVfR04oar/UdWbgC4RrpcxMcu6hUys8dI1lH1qs0NE+uHcC2DTVBqTD+sWMrHGSyB4XESqAffh3D9QFbgnorUyJtwGDy62XVm3kIk1XmYoy84vtA84P7LVMSZCbr892jUArFvIlEwhxwhEpLmIfCEia93XbUXk4chXzZgwOnzYeUSZdQuZksjLYPGrwAO4YwWquhonXYQxseOSS5xHhO06cDzfskBZGI0pCbwEgkqquiTXssxIVMaYWPe/3YfyLcudeteYksJLINglIk1w/45FZBCwI6K1MiZGnTiZ/9d9vepBEg8ZE0Verhq6A3gFaCkiGcBm4LqI1sqYGBSsWwhgRJ8WxVQTYwrGy1VDm4ALReQUIEFVD0S+WsbEnmDdQmADxabk8jJ5/V+AN4EDwKsicjZwv6rOiXTlSosF7//Arm0Ho12NuNbgtF4AbP37iojto8ZxAOGPB5UykgVApiayX8tTJjGBqRHctyk+tetXpsfgPBMpxjQvXUM3qeoLItIHqAvciBMYLBCYmLG144CIbn/XwWNByxvWqhTR/RtTFF4CQfZVb5cAb6rqKhGxK+EKoLSdPcSkXbucn7VrR2Tz7cfMoVcZZ6B4amUh57/WcbY82jUi+zUmHLwEguUiMgcn++gDIlIFZzYxY2LHIHcupQjNR2BpJUws8xIIbgaSgU2qelhEauF0DxljPLC0Eqak83LV0Elghd/r3cDuSFbKmNLErhYyJZ2XG8qMMUGEmn/AmJLOAoExRRRq/gFjSrp8u4ZEpAIwDGgKrAFeV1XLMWRi0223RWzTNlBsYl2wMYJ/4mQcXQBcDJwJ/KU4KmVM2A0ZEpHN2rSUpjQIFgjOVNU2ACLyOpA7A6kxsWPbNudn/fph3axNS2lKg2CBwNfeVdVMu4fMxLTrr3d+hvk+AusWMqVBsEDQTkT289udxRX9XquqVo147YwpwaxbyJQW+QYCVU0szooYE2vGzl4ftNy6hUys8JJ99HzgLJyJab5V1dRIV8qYWJCx90i+ZdYtZGJJsMtH6wEfA0eB5ThdQoNFpCJwharaXTQmrgn5Tz9p3UImlgRrEUwEXlbVyf4LReQG4B/AZRGslzHhdd99Yd3ctLSMoHMQW7eQiSWhLh+9IvdCVX1LRB6KYJ2MCb/+/cO6uezLRv9MeZrhDKdtc2/Uf5FK7P30R6r3bxLWfRoTKcFSTAQcLBaRhPzKAqzbV0TWi8hGEbk/yHodRSRLRAZ52a4xBbZ+vfMIk2CXjZZJsEutTWwJ1iL4VEReBe5W1UMA7rzFzwMzQ21YRBKBl4CLgHRgqYhMV9XvAqz3DDC7cIdgjAd/+pPzM8z3EUzgt5nJrnCn6TjjqmZUt64hE0OCtQj+CuwD/iciy0VkObAF2A8M97DtTsBGVd2kqseBdwk8rnAX8BHwS0Eqbky0hLp/wMYHTKwJdh/BCWC4iPwNJ/Gc4HyxH/a47XrANr/X6UBn/xXcK5OuAH4PdMxvQyJyK3ArQIMGDTzu3pjIsGyjprTJt0UgIlVFpJmqHlHVNUALYJCI3CAip3rYdqCO0twXWowHRqpqVrANqeorqtpBVTvUqVPHw66NiRwbHzClTbAxgueAb4AN7usngVlARaAbTorqYNIB/wxfScD2XOt0AN518xjVBi4RkUxVneal8sYUt1DdQg1rnVJMNTEmfIIFgo7An/xeH1TVuwBE5GsP214KNBORRkAGcDUw1H8FVW2U/VxEJgOfWRAwEfHww2HZTKhuodpVyoVlP8YUp2CBoIyq+nflXO/3vHqoDbsZS+/EuRooEXhDVb8VkWFu+aRC1NeYwrnwwrBsxrqFTGkULBCcFJHTVPUnAFVdC74B3pNeNq6qM8l1qWl+AUBVU7xs05hCWbnS+ZmcXOhNWLeQKa2CBYKxOPcS3AekucvOxhk7GBvpihkTVnff7fwswn0E1i1kSqtgl49OEZFdwOP4ZR8FHlHVz4upfsaUGDYJjSmtgqahVtVZOFcKGWOCGDXgLI5N/zLa1TCmUILdWWyMcT08bU3Qcrub2MQyCwTGePD2oq3RroIxERNyhjJjSoUnnyz0W0PNPWDjAybWeZmq8kngWVXd676uAdynquG5Q8eY4tCtW6HfmvtqoR67v6b28d2+12e1bgn0LvT2jYk2L11DF2cHAQBV/RW4JGI1MiYSvvnGeRRCsKuFEoAmdSoXslLGlAxeuoYSRaS8qh4DcOcsLh/ZahkTZg8+6Pws4H0EgW4iW1DrXN/z8UOSOd8Gik2M8xIIpgBfiMibOPcS3AT8M6K1MqaEGPnR6qDldrWQKQ1CBgJVfVZE1gAX4KSWfkxVbTYxU+pNS8vgWGb+2VRskNiUFp6uGnLvJLa7iU1cCZVSYtSAs4qpJsZEVsjBYhHpIiJLReSgiBx3J5nfXxyVMyaagg0Sg3ULmdLDS4tgIs5cAh/gTCRzA87UlcbEjvHjC7R6qEyj13WxKVNN6eG1a2ijiCS6U0q+KSKFuw7PmGgpYPrph6YGTynx+OVtilAZY0oWL4HgsIiUA1aKyLPADsASr5vYMm+e89PDBDXT0jI4dDz/abRt+hlT2ngJBNfjjCXcCdyDMw/xwEhWypiwe/xx56eHQBCqNXCtdQuZUiZoIBCRROAJVb0OOAqMLpZaGRMloVoDYN1CpvQJetWQOyZQx+0aMqbUC9UasHsHTGnkpWtoC/BfEZkOHMpeqKrjIlUpY6IlVGvA7h0wpZGXQLDdfSQAVSJbHWOi59pXFwYtr1g2we4dMKVSvoFARP6lqtcDe1X1hWKskzHh93//F7R4WloG//1xT9B1nrqybThrZEyJEaxFcI6I/A64SUTeItdVc6oa/L/GmJKkRYuAixe8/wO7th1k6ZY9DDmZ/1BYgoB++TNTv/wZgNr1K9NjcPOIVNWY4hYsEEzCmbi+MbCcnIFA3eXGxIZPP3V+9u+fp2jXwWNknQw2Bxk0tjkHTCmWbyBQ1QnABBF5WVVvK8Y6GRN+f/+78zNXIOgxuDk3PjiTzCr5B4KyCbDh0a6RrJ0xURUy6ZwFAVOaPTxtDZkhWgNjr0ounsoYEyVepqo0ptSasmhr0HK7UsjEAwsEJm6FulwU7EohEx8sEJi49PC0NSEvF+3epKa1Bkxc8JSG2piY969/+Z5OS8sI2SUE8PYfbYDYxIeItghEpK+IrBeRjSJyf4Dya0Vktfv4RkTaRbI+Jo7Vr+88CD0hPdjEMya+RKxF4GYufQm4CEgHlorIdFX9zm+1zcB5qvqriFwMvAJ0jlSdTBx77z0AHi5/ZtAJ6cE5O7IMoyaeRLJrqBOwUVU3AYjIu8BlgC8QqKr/TGeLgKQI1sfEs5dfZtfBY0y58OGQq44bkhz5+hhTgkSya6gesM3vdbq7LD83A58HKhCRW0VkmYgs27lzZxiraOLJxl8OhlzHBohNPIpkIAg0o1/AO3dE5HycQDAyULmqvqKqHVS1Q506dcJYRRMvlv/v15DrJGADxCY+RTIQpONMa5ktCSeddQ4i0hZ4DbhMVXdHsD4mTl376kJOZAUfFwDrEjLxK5KBYCnQTEQauTOcXQ1M919BRBoAHwPXq+oPEayLiVNe7hcA6xIy8S1ig8WqmikidwKzgUTgDVX9VkSGueWTgEeAWsA/RAQgU1U7RKpOJr48PG2N736B2y5/IN/1mtU9xbqETFwT1eAJt0qaDh066LJly6JdjYKb1yvvsgaDofntkHkYUi/JW944xXkc3QVfD8pb3uw2+N0QOLQNFl6ft7zlfZDUH/avhyV/ylve+mE47UL4dSUsvztvebsnoU432PkNrHowb/k546FGMvw0D9Y+nre80/9B1RaQ/ims+3ve8q7/glPqw//egw0v5y0/90OoUBs2TXYeufWaCWUqwQ//gK3v5yjadfAYHRY5Vwj9sfbHXFB1SY7yoyfLk7JlNKdWKcfiAYvg5y9ybrt8LejxkfN85QOwK1c6ikpJ0G2K83z53fDrSn7ZsgmAug0bQ5Xm0PkVp3zxrXAgV4O3RrLz+QF8cx0cTs9ZXrsrJD/lPF8wEI7l6jU99QJo8zfn+VcXQ9aRnOX1LoVWw53n9reXt7wof3sXpuZdPwaIyPL8TrTtzuJisnJl3mX7tsB5zeHwYfghQPnB7XBuY9i9B7YFKD+8E7r9DrbvgF8ClB8/AJ2SYNNm2B+g/ORxOPsSWL8ejgQoT0yANr+HNWsgK0B5xcrQogusWAEJ3+Utr1oHGreDJUuh3I95y+s2hDOawjffQKWMvOX1z4RaZ8DXX0PlX/KWN+8ElarCf+ZDlZ+Pgt84QOZJ5ZkF/wAgq9Ep1D3oXDG0K7k2AGVOlOWZBf+gS+NazN5TllNPz7ntowpdejjPU1OhembO8kMK3bs5z//zH6h2Eo4fPQOA7XthP9DTvSNm/gKomqvu+xLgvHOc5//9L5yS69KKvWWgV7LzfNEiqJC7vAL0cm91WLoMyuYu3wC9WjnP7W8vb3lR/vaSL8y7fqyzQFBM7v4sNc+ywYPhPIAylQKWp6TAuYCWqx2w/DY3QXhW+foBy++7z/l5okKLgOUPJzs/j1RIDlj+pPtFd6BCNx4MUD7e/YfYU+5CHv8s73/H/7mp/38u05+/f5Z3Qph/DXF+bksYwsufDclT/mGK83PjyRQmf5aSp3zm7c7Pb4/fzpTX+3Hy0GEADh3LJEuVR1s73UEzv+5D0/TNAKR89ToA5RKOM76Lc8b9xaobWLL8vBzbrlUL3PYAs39+ioW5GgRJSdDdfT5163hWriRHi6B5c+jplk/54RV+yNUgSE52f/fAy6umkJ6rQdC1K/Ryn49d9BG7czUILrjgt/JHvvycI7kaBJde+lu5/e2F928vdXie1WOedQ2ZUuWicals+OVQnuXvvuNkOLl66NOAMy4w995eYd33e6OdfQx59OmwbteYcLCuIRMXOj8xl58PHA+5XiSCgDGxzNJQm1LBaxBIAAsCxuRigcDEvIvGpXoKAmA3jRkTiHUNmZjmtSWQctUorulUn0ftpjFj8rBAYGKW1yAAMKhHcx611NLGBGRdQyYmFSQIXNelAY9vXwD/+EeEa2VMbLIWgYkp/mkjvLiuSwNnkpledzkLbr89QjUzJnZZIDAxI797BPLjCwLGmKAsEJgSb1paBne/t7JA77EgYIx3FghMiVbQVgBYEDCmoCwQmBLp2lcXeppHIDcLAsYUnAUCU6IUdDA4mwDPD0nOf3KZ1NQi1cuLur9rHPF9GBMJFghMiVDYAAA4cwo8dFGYa1Rw56fcGu0qGFMoFghMRD2z5BnW7VmXb/nmXYf4ef9RACo2KPj2q1UsS6vTq3LjrHdoWbMlIzuNLGxVjYlbFghMVOw6eIyNvxws9PsFaFK3MrUrlw9fpYyJUxYITEQFOkMvzJVA/ro3qWlzDBsTRhYITLEpyjgAWAAwJlIsEJhiUZRWQEkZDDamtLJAYCKqMHcFZ0sQGDc4yCWhxpiwsEBgIqawN4WB3RhmTHGyQGAioiBporOFvCnMGBMRFghMWBW2K8gGgo2JHgsEJmwK0xVkA8HGRJ8FAlNkhb0s1FoBxpQMFghMkbR9dBb7j2UV6D12NZAxJYsFAlNgRbkxrFndU5h7b6/wVsgYUyQWCIwn09IyGPHBSk6cLPw27JJQY0omCwQmj6KmgsitavlEVo/uG7btGWPCK6KBQET6Ai8AicBrqvp0rnJxyy8BDgMpqroi3PUIx9msKRzrCjKm5ItYIBCRROAl4CIgHVgqItNV9Tu/1S4GmrmPzsDL7s+wKUqKA1N4ZRKE565qZwPCxsSASLYIOgEbVXUTgIi8C1wG+AeCy4C3VFWBRSJSXUROV9Ud4arE2NnrAXikzFucmfC/cG3WBNG0jjtPwEqcx2lt4OKng7/JGBM1CRHcdj1gm9/rdHdZQddBRG4VkWUismznzp0FqsT2vUcKtL4pvKoVytClUS2bLMaYGBPJFoEEWKaFWAdVfQV4BaBDhw55yoM5o3pFMvYeYUzmDQV5mykAuxrImNgWyUCQDtT3e50EbC/EOkUyok8LGyOIAPvyN6b0iGQgWAo0E5FGQAZwNTA01zrTgTvd8YPOwL5wjg8AvsFKu2qocMqXSeCZgW1t0NeYUixigUBVM0XkTmA2zuWjb6jqtyIyzC2fBMzEuXR0I87lozdGoi6Xt69nX2TGGJOPiN5HoKozcb7s/ZdN8nuuwB2RrIMxxpjgInnVkDHGmBhggcAYY+KcBQJjjIlzFgiMMSbOiTNeGztEZCdQ2FwRtYFdYaxOLLBjjg92zPGhKMf8O1WtE6gg5gJBUYjIMlXtEO16FCc75vhgxxwfInXM1jVkjDFxzgKBMcbEuXgLBK9EuwJRYMccH+yY40NEjjmuxgiMMcbkFW8tAmOMMblYIDDGmDhXKgOBiPQVkfUislFE7g9QLiIywS1fLSJnR6Oe4eThmK91j3W1iHwjIu2iUc9wCnXMfut1FJEsERlUnPWLBC/HLCK9RGSliHwrIv8p7jqGm4e/7Woi8qmIrHKPOSJZjIuLiLwhIr+IyNp8ysP//aWqpeqBk/L6R6AxUA5YBZyZa51LgM9xZkjrAiyOdr2L4Zi7ATXc5xfHwzH7rfclThbcQdGudzH8nqvjzAvewH1dN9r1LoZjfhB4xn1eB9gDlIt23YtwzD2Bs4G1+ZSH/furNLYIOgEbVXWTqh4H3gUuy7XOZcBb6lgEVBeR04u7omEU8phV9RtV/dV9uQhnNrhY5uX3DHAX8BHwS3FWLkK8HPNQ4GNV3QqgqrF+3F6OWYEqIiJAZZxAkFm81QwfVZ2Pcwz5Cfv3V2kMBPWAbX6v091lBV0nlhT0eG7GOaOIZSGPWUTqAVcAkygdvPyemwM1RCRVRJaLSKxP1u3lmCcCrXCmuV0D/EVVS/N8hGH//oroxDRRIgGW5b5G1ss6scTz8YjI+TiB4NyI1ijyvBzzeGCkqmY5J4sxz8sxlwHOAS4AKgILRWSRqv4Q6cpFiJdj7gOsBH4PNAHmisgCVd0f4bpFS9i/v0pjIEgH6vu9TsI5UyjoOrHE0/GISFvgNeBiVd1dTHWLFC/H3AF41w0CtYFLRCRTVacVSw3Dz+vf9i5VPQQcEpH5QDsgVgOBl2O+EXhanQ70jSKyGWgJLCmeKha7sH9/lcauoaVAMxFpJCLlgKuB6bnWmQ7c4I6+dwH2qeqO4q5oGIU8ZhFpAHwMXB/DZ4f+Qh6zqjZS1Yaq2hD4ELg9hoMAePvb/gToISJlRKQS0Bn4vpjrGU5ejnkrTgsIETkVaAFsKtZaFq+wf3+VuhaBqmaKyJ3AbJwrDt5Q1W9FZJhbPgnnCpJLgI3AYZwzipjl8ZgfAWoB/3DPkDM1hjM3ejzmUsXLMavq9yIyC1gNnAReU9WAlyHGAo+/58eAySKyBqfbZKSqxmx6ahH5N9ALqC0i6cCjQFmI3PeXpZgwxpg4Vxq7howxxhSABQJjjIlzFgiMMSbOWSAwxpg4Z4HAGGPinAWCGCYi1UXk9ghuP0VETro3omUvWysiDd3nW0SkdiG2O0ZELgywvJeIfFakSgffb7KIXBKmbZ0jImvcDJATJMityyLSQEQOishwv2VPiMg2ETmYa93ficgXblbJVBFJ8lu+3C+r6LAi1L2hiBxxt7VKnGy0LdyyDiIyobDbLkAdaonIV+7nMjFXWcDPVkTKi8h77vLF2X+HbtkfRGSD+/hDpOtf2lggiG3VgYCBQEQSw7SPdOChMG0LAFV9RFXnhXObHiXjXH8dDi8DtwLN3EffIOs+T97cTp/iJFTL7TmchGJtgTHAU+7yHUA3VU3GuUnsfhE5o9C1hx9VNVlV2wH/xMngiaouU9U/F2G7Xh0F/gYMD1CW32d7M/CrqjbF+UyfARCRmjjX2nfG+UwfFZEaEa19KWOBILY9DTRxz+zGumfUX4nIO8Aa98zPdzORiAwXkVHu8yYiMss9y1wgIi3z2cdnwFnZZ4yhiEgnEfnYfX6Ze+ZZTkQqiMgmd/lkcecGECfX/DoR+Rq40m87p4iTl32piKSJSJ7Mou7Z4SV+ryeLyEB3X2+6Z5VpInK+OHeljgGGuJ/XEC/7yOcYTweqqupCN63BW8Dl+ax7Oc5drt/6L1fVRfncDXom8IX7/CvcTJuqelxVj7nLy+Phf9f9PCa4Z/ybJP/5GKoCv7rv8bXKRGSU+/mkuu//s7v8FBGZ4bYm1orIkFB1yU1VD6nq1zgBwb/OwT7by3CCFjh3il/gthb6AHNVdY+bYXcuwQOzyaXU3VkcZ+4HWrtniYhIL5wzotaqutm/6RzAK8AwVd0gIp2Bf+Ak7crtJPAszhmjlyb3CqC9+7wHsBboiPO3tth/RRGpALzq7ncj8J5f8UPAl6p6k4hUB5aIyDw3h062d4EhwEz3i/4C4DbgDgBVbeMGuDk4WTkfATqo6p3u/p8MtA+c3C3+dfHXCyfTY7rfsoDZH0XkFGAkcBGBz3wDWQUMBF7AyZxaRURqqepuEakPzACaAiNU1Ut+mdNxEgy2xElN8KG7vImIrASqANmpKAJpCZzvrrdeRF7G+ZLdrqr93OOslvtNIjICuDbA9uaHaHEE+2x9WTfdO4734dwtX9qyCRc7CwSlzxJV3RxsBRGpjDNRzQfyW9d2+SBveQd4SEQahdq5+w+6UURa4QSlcTgTbSQCC3Kt3hLYrKob3HpNwekSAOgNDJDf+tUrAA3ImTfnc2CCiJTH+XKar6pHRORc4EW3PutE5H84gSC3gPtQ1e9xupECEgk4HhDoFv3RwPOqejDwWwIaDkwUkRRgPpCBm1tfVbcBbd0uoWki8qGq/hxie9PclMzfiZOHJ9uPficQQ3BODAKdRc9wWyLHROQX4FScVM/PicgzwGeqmvv3iqqOBcZ6PWg/wT7b/MpKWzbhYmeBoPTxP2POJGcXQgX3ZwKwN/uLIBT3y/3vOGe3eYjIbJwviGWqegvOF/7FwAlgHjAZJxAEOivO7x9WgIGquj5IvY6KSCpO18AQ4N9+7/Ui4D7cbrBgLYJ0ck7sk1/2x87AIBF5Fmc856SIHFXViQHWBcA9y7/SrUdlt377cq8jIt/itLg+zLuVHI75Pc/vc5kOvOnh/VlAGVX9QUTOwRlveUpE5qjqGP83FaFFEOyzzc66mS4iZYBqOBO4pOP8XvzfkxpkHyYXGyOIbQdwmuz5+RmoK84VGuWBSwHcPO2bReQq8M2BGmoO48nAhThTAeagqn3cgcdb3EXzgbuBhaq6E6f53pJc/eTAOqCRiDRxX1/jVzYbuCv77FtE2hPYuzhJt3q478ne/7Xu+5rjtCTWk/fzCrgPVV3vHk+gx163b/+AiHRx33sDTtbP3J9LD7/sp+OBJ4MFAbcOtUUk+//yAeANd3mSiFR0n9cAurvHhIg8JSJXBNtuCOfiTAfpidsiOayqU3AGt/PMmauqY/P5/IIORIf4bKfzW/fkIJxuPcX5PfYWkRruZ9Ob3/4WjAcWCGKYO6fAf90BuzzNcFU9gTNAuhhn0HedX/G1wM0isgrnCzroQKk60wROAOr6LS5DzjPGbItxWgjz3dergdWaK8Ohqh7F6QqaIc5g8f/8ih/Dybi4WpwB78fyqdocnK6neW4dwRnvSBQnG+V7QIrbvfEVcGb2YHEB9hHIbThzO2zE+RL9HEBEBojImGBvdNd7VpzMkpVEJF3cQXycM9v1IvIDzmf4hLu8FbDY/X39B3hOVde4ZW2AnwpQd/jtIoNVwJPALaHe4KcNznjKSpyxnMcLuG/AufwYp+swxf0MznSLAn62wOtALRHZCNyLM0aGqu7B+d0tdR9j3GXGI8s+agpFROoAK1XVBuWiTERmq2qfaNfDxC5rEZgCE5EBOOMAD0S7Lsbpmot2HUxssxaBMcbEOWsRGGNMnLNAYIwxcc4CgTHGxDkLBMYYE+csEBhjTJz7f2ZyZhfrmTDCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "\n",
    "#NOW, CONVERT THE UNSMEARED VOTE TO AN S(V) CURVE - and find the # of GOP seats at the statewide vote\n",
    "cumSeats = [0.]*(nDistricts+1)\n",
    "for d in range(nDistricts+1):\n",
    "    cumSeats[d] = float(d)/float(nDistricts)\n",
    "    xValues = [stepStart[d],stepStop[d]]\n",
    "    yValues = [cumSeats[d],cumSeats[d]]\n",
    "    plt.plot(xValues,yValues)\n",
    "    if stateGOP > stepStart[d] and stateGOP <= stepStop[d] : #is the statewide vote in this step?\n",
    "        expectedSeats = float(d)/nDistricts\n",
    "\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"frac GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\" exp seatFrac, no smear\")\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\" exp seatFrac, smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fractional-seat-smeared (var of 0.04 ) responsiveness is 2.007\n",
      "fractional expected GOP seats =  2.079  out of  12 seats for NJ\n",
      "simpler expected GOP seats =  2.0  out of  12 seats\n",
      "to repeat from earlier block, non-smeared and smeared fraction of seats = 0.167 0.173\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's  ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE\n",
    "#unlike Home District code, we do not attempt responsiveness for unsmeared -- too stair-steppy\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "#print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional-seat-smeared (var of\",seatVar,\") responsiveness is\",round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n",
    "print(\"to repeat from earlier block, non-smeared and smeared fraction of seats =\"\n",
    "      ,round(expectedSeats,3),round(smearedSeats,3) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save these fabulous results to a file\n",
    "params = [STATE+\"-\"+str(nDistricts)+\"d\",\"totalGOPseats\",round(nDistricts*smearedSeats,3),\"fracGOPseats\",\n",
    "          smearedSeats,\"nTracts\",nTracts]\n",
    "for p in range(nDistricts - len(params) ):\n",
    "    params.append(\"blank\")\n",
    "\n",
    "df = pd.DataFrame( {\"parameters\":params,\"seatPctGOP\":EDvGOP} ) \n",
    "\n",
    "outname = STATE+\"enacted.csv\"\n",
    "outpath = \"enacted_analysis/\"+outname\n",
    "df.to_csv(outpath)\n",
    "#SOMEHOW, THIS IS A SORTED LIST, NOT SURE WHERE IT GOT SORTED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "0af834a7-8b87-4bb9-92b8-d400fdcc1ce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6122725899841236,\n",
       " 0.5256773381854366,\n",
       " 0.47959749788904144,\n",
       " 0.4372265645587057,\n",
       " 0.42723460256808093,\n",
       " 0.41303605758650064,\n",
       " 0.4063293882649418,\n",
       " 0.4024856658179458,\n",
       " 0.3762191064630529,\n",
       " 0.3250965133559313,\n",
       " 0.2743920436164206,\n",
       " 0.18735677903675224]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EDvGOP  #confirming that this got sorted somehow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f4d302f-1bb7-4f40-868b-9f19c4568e62",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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